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SSP Innovation Showcase (Summer 2025)
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SSP Innovation Showcase (Summer 2025)
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Language: EN.
Segment:0 .
All right people are starting to come in.
We're going to give it just about a minute before we get started. If you have a moment and you're inclined, please let us know where you're coming from today. Put it into the chat. Thanks Seattle. Orlando beside Richard.
Let's see who's from the farthest. Oh, we have Oxford. Yeah there you go. That's Richard. Yeah all right. We have. Just about a few more seconds here, and then we'll get started.
All right. Hello and welcome to today's SSP innovation showcase. I'm Dave Myers, CEO of data licensing alliance and a member of the SSP education committee. Before we get started, I have just a few housekeeping items to review. Attendee microphones have been muted automatically. Please use the Q&A feature in Zoom to enter questions for the moderator, myself, and panelists.
You can also use the chat feature to communicate directly with other participants and organizers. Closed captions have been enabled to turn on captions. Select the CC button on your Zoom toolbar. Again, before we get started, a quick note on SSPS code of conduct and today's meeting. We are committed to diversity, equity, and providing an inclusive meeting environment that fosters open dialogue and the free expression of ideas free of harassment, discrimination, and hostile conduct.
We ask all participants, whether speaking or in the chat, to consider and debate relevant viewpoints in an orderly, respectful and fair manner. Now, I'm pleased to introduce our first panelist, David Turner, publishing automation and digital transformation specialist, data conversion laboratory. All right. Howdy, everybody. And Thanks so much to SSP for giving me this opportunity.
As Dave said, I'm David Turner, and I'm a digital transformation consultant and head of the partner relationships at data conversion laboratory. Also known as DCL. I am proud to present our latest solution, which is content crystallizer, which essentially helps a scholarly publication, whether it's a journal article or a book chapter, conference proceeding, or what have you, it helps it to come together, to take shape, to crystallize into a publisher's desired form and format, with the goal of letting you go from word to XML and in 3 easy steps.
So before I get too much further, I do have a quick poll, and I'm not exactly sure how this whole poll thing works, but I think Susan pushes it out there. Yes, we want to know, does the x-files end of life announcement affect you. Yes no. Or what the heck is x-files? The reason that we're asking this is that content crystallizer does a lot of things that are similar to what x-files does.
It actually came about in response to the end of life announcement. So just interested to see what we're working with here. And Susan, I'll just wait for you to post the results whenever you want to say, Yep, this is done.
All right. We got a whole bunch of people that don't know what extyles is. That's great. And others that yes. Have been affected by this. A small percentage of nos. OK, well, let's just dig in. So for those of you who don't know, extyles extyles is a word plugin that's developed by a fantastic person in the industry, Bruce Rosenblum, who sadly passed away a couple years ago.
And it's a tool that's been used successfully in the industry for many different things. Right? auto styling documents, cleaning them up, fixing reference formats and then taking that and converting the content into XML. Bruce's company and sold to add upon a couple of years ago. And just recently, it was announced that they're going to be retiring this tool. So one of our partners came to us and said, hey, do you guys have any kind of tool that could help some of these people out that are looking for help and we don't have any kind of word plug-in, but I, I, I got to looking around and thinking, you know, we actually do have a lot of software in our existing processes.
And you know, and behold, they do a lot of the same things. It's not a word plugin, but it is commercial quality software that we use. And it's not like a single piece of software. It's software that we've used. And, you know, it's not really been configured for external use. So we got to thinking, you know, is there a way that we could do this in a self service manner.
Because obviously we could just tell people, Oh, send it to us, and we'll, we'll do it as an outsourced service. I mean, DCL does that kind of thing all the time. But this is not what that's about. We're looking to try to replicate something where someone has a do it yourself kind of a solution, something that is fully automated, at least from the DCL part of the process, where they can get results in minutes as opposed to, days or hours or whatever it is that we do in another process.
So we came up with this idea of a, a content vending machine, if you will, right? Where the software exists on our servers. You put in your content. Some automation runs, a few minutes later, you get back results. You can make some changes, run it again, get results. So it wasn't a very heavy lift for us because we weren't really creating new software.
We just have been putting existing tools together in a new way, and that's what we've got. And it's resulted in this three step process on this next slide, where basically you come in and you, you, you start by getting the document prepped and that sort of kicks off the crystallization process. Then step two, you let the editor make some fixes, work with the authors to make some edits, and then ultimately load it up again.
And step three then takes you actually from the Word document into XML. So at a little more of a deeper level, the way we started with a Word document, a raw word document, it doesn't have to be styled or anything. It could be a journal article, book, chapter, what have you. But the idea is that the author then loads that as a zip file with a manuscript supplemental file, some basic metadata, and that kicks off this automated doc prep, which does a ton of different things.
Right so it's intended to really start by applying styles automatically to all the different elements of the document. Tables, headings. Author names. Affiliations it goes through and it cleans up spacing problems. Removes blank paragraphs, puts metadata in the right places, checks to make sure the metadata is there. Looks up things on PubMed, Crossref, or peer review system.
It checks citations for completeness, checks the order of citations, identifies unrecognized styles, does all of these things, and then ultimately delivers back a styled word document. And we've tried to match the styles, look and feel also with comments and error reporting integrated into that Word document. So the document is starting to crystallize. It's gone from raw into something that is styled cleaned up and ready to work with.
At this point, the editor then starts to work with it. They incorporate whatever comments they can override elements that they like, they can share back and forth with an author. And when they're done, then they can either run back through auto prep again and have it. Do some other additional checks, or they can move along and they can select the actual conversion. Option and take this thing onto XML.
Now before it does the full XML, the word XML process starts. By doing a check to make sure no errors have been introduced. If if there have been errors that have been introduced, we send back the document with a report. But once it does pass that step, then it moves into an automated conversion process. It converts the document XML. It takes that XML, it parses it against the DTD, and then it runs a series of additional checks, bunch of checks on the XML to make sure that all of these things, all of the styles, everything that the client has set up does match and that they're getting really useful XML.
The whole process takes, you know, minutes. It's, you know, 5 to 10 minutes for part one, it's like 5 to 10 minutes for part two. If anything fails along the way, we send it back to the client with suggestions and comments. But ultimately the result is you get this valid and useful XML. Now, I can't go into all the details in the time that we have here today, but I did go ahead and put together a list.
I violated my PowerPoint rule about amount of text on a slide just so I could help you. Anyway, this is by no means a comprehensive list, but some of the things on the auto doc prep it's doing, it's using AI to auto apply these word styles to all the different elements of the article. It's doing things like verifying that citations have a match. If there's a citation listed at the end, is there a matching citation in the article.
Are the are there other titles where they're supposed to be titles. Do we have the author's name. Do we have the, you know, the affiliations. And then, depending on what the client wants us to do, we can either fix certain things automatically or we can suggest the fix in the comments. Finally, it does things like look for things that are out of place.
So for example, like we found an author name in the middle of a table or an abstract that was like in the complete wrong place in the document or an image floating somewhere. So it does all of those. And then as far as on the XML side, it goes through and it checks like I mentioned before, checks for that suitability first. Then it goes through and it actually creates to make sure that we've got good XML right.
But more importantly, it goes in and it actually checks to make sure we don't just have valid XML, but we have good XML, right. So you could technically have abstract. And then the text insert abstract here and then a close tag of abstract. And that would parse but that wouldn't be useful. XML so we can check all of those things. There are a ton of checks I didn't list here, and we can also customize checks too.
So anyway, I'm pushing up against my 10 minutes. So that's going to take me to my last slide. And if I can actually click the button correctly. While you're scanning the QR code let me just quickly summarize. We provide similar functionality to extyles, but it's not a word plugin like extyles users are used to using. It's a fully automated solution, and it's really geared to help that unstructured word document crystallize into a structured word document, and then that structured word document crystallize into a useful XML.
It can be customized in all sorts of ways how things are ingested, what are the styles are how you want. You know, what automations you want, what checks you want, what XML model you need. You know how you want things reported. And it doesn't just have to be customized at the publisher level. It can also be customized at the publication level.
So if you have a, you know, a couple of journals that you'd like treated different ways we can accommodate that. Or if you have, you know, books that are maybe in different product sets, we can, you know, accomplish that altogether. In any case, that's our presentation. Thanks so much for listening. And Thank you, SSP for this opportunity.
Great Thank you David. Our next presenter is Samantha green, director of product marketing at Silverchair. Thanks so much, Dave. Hi, everyone. As as mentioned, I'm Samantha green. I'm the director of product marketing for Silverchair, and today, I'm really excited to talk to you all about scholar one gateway.
If you were with any of our team at SSP. You may have heard some about this, and I'm excited to get a little bit more into the weeds and talk a little bit more. This is one of our first really tangible steps towards modernizing scholar one, in this case, really focusing on a centralized hub for the author experience. So I thought it would be great to start with just a little bit of a level set on why this modernization matters so much.
And to put it simply, it's because peer review and publishing workflows take around 130,000,000 hours each year, and this is from 2020. The statistic is from 2020. So who knows what kind of inflation has hit this figure in the last five years. And to put that into a different kind of perspective, that's a continuous 15,000 years. So every year and with so much time being spent on these processes, these tools in.
In this technology, the need to create a modern, streamlined experience is really critical. This process takes a lot of time and care, and that time should not be spent wrestling with technology. So to dig a little bit further than that, that statistic, I think in general, the publishing process and publishing infrastructure is a little bit of an unbalanced experience for authors.
Reviews are not evenly distributed. It can be really challenging to conduct reviews in a timely manner. Authors have stated that the submission process can be frustrating and inconsistent across different technologies. The process in general is slow going, and I think publishers have made great strides to shorten that time to publish, but it can still be very complex and challenging to do so.
And in general, submission and peer review platforms have been slow to evolve and can be outdated at times, frankly. Infrastructure like scholar one in general is really big. It's incredibly complex, and it's so important that it's challenging to kind of approach it and changing it in any kind of way that could engage and mitigate risk. So we wanted to look at the process holistically, mapping those pain points and challenges for authors throughout the process, and really think about how we could transform this technology for today's users, but also tomorrow's creating a nice future proofed system.
So our vision for gateway and the future of scholar one more broadly is creating tools that help publishers build community. So in this case, this is an author community. That means giving authors a modern and simple experience that is intuitive and simple. Publishing and peer review, I think, can be rigorous and in-depth without being hard to complete, without those sort of manual tasks being a challenge and adding to the Time and Labor intensive nature of the process.
Building that community also means attracting folks into the platform with configurations and integrations to ease the process. And importantly for publishers who have large portfolios, gateway is a centralized publisher hub with a single view of submissions and reviews across the entire publisher's portfolio. And gateway continues the product development legacy that is so critical to Silverchair and really centers it on community feedback, user feedback, and really leading from what we hear from our publishers and from our users to drive progress.
So our design principles for gateway are pretty simple. Getting published is really exciting for authors, and we want to do what we can to kind of extend that joy to the process. Maybe we can do that by making it more engaging, bringing clarity and transparency to the process and aligning, really aligning the experience of users using scholar one with the same level of UX and technology standards that our users expect, and that they see in every other aspect of their lives with streamlining those processes.
We also bring new efficiencies, new configurations, new integrations that can really extend the reach and the impact of scholar one technologies and scholar one gateway really opens the door to the future of the publishing process. Now, today, gateway is an option across dozens of journals with our development partners. These development partners are publishers who work within the scholar one ecosystem.
And they've been really critical to the creation of gateway. Surfacing real challenges and pain points from their author communities. And in the future, we intend to use this same kind of ethos, these same kind of design and development principles to editors and reviewers to really have gateway serve as the keystone for the modernization of the platform overall. And now I really think it's past time for me to show you what gateway looks like.
So here is the sort of centralized hub itself that you hit when you first log in to gateway. It's highly customizable for publishers, both on the aesthetic level. So you can carry brand colors, fonts, iconography throughout the site, building up that recognition and creating a really customized experience for authors. But you can also customize things beyond the aesthetics, things like author resources, key widgets that can be added throughout the page to create the unique experience that your community needs, especially for things that are more timely or, you know, different types of calls for papers and things like that.
There's a lot of flexibility here. The centralized nature of the homepage also offers really quick access for an author to everything in the publisher portfolio. You can see the find a journal right at the top to help authors orient themselves and get where they're going more quickly. It's really easy to start a new submission, and above the fold there's a dashboard for, you all of the work that's in progress for an author with quick and easy access to continue that work.
Now on to my favorite aspect of gateway. I think it's a great working space for authors. So this is an account view for an author. And from this dashboard they're able to see everything that they need to do, actions required, but also monitoring all other submissions that are in process with other stakeholders in the peer review process. So you can also see this.
This covers both authored articles as well as articles for review, and it's really easy to see the next step for each manuscript. So excuse me, you can see the status of articles at the bottom, awaiting checklists with different peer reviewers and right at the top, you can quickly click into whatever action is needed to continue a submission, whether that's submit or continue your draft.
So as I mentioned, we've built gateway in partnership with publishers so that it will truly benefit all stakeholders. Authors and reviewers, get personalized experiences, easier submissions, and a process that is easier to keep track of. Overall, publishers get really robust user insights that they can use to make changes and directly address new pain points, and they get really powerful integrations to extend their impact and a more customizable brand experience.
And like I mentioned earlier, we're all about bringing joy to this process. But at the bare minimum, creating a place where peer review and editorial management. Technology isn't a pain point and isn't a challenge in the process. And and going from an anchor to a partner will be a really great place for ScholarOne going forward. Ultimately, we see gateway as the first step to transforming the publishing experience in ScholarOne.
Right now, we are expanding the pilot across more journals with our development partners and conducting a series of adoption campaigns to gather more user feedback. We will then obviously use that user feedback to continue to develop gateway and to develop the roadmap beyond gateway. And we're also exploring how to bring some of these same principles of our author focus to editors, to reviewers conducting work along those lines in parallel.
And up next, by the end of the year, we expect to have the plan in place for the full launch of gateway across the scholar one platform. And lastly, you can always see more and learn more by clicking through. Here we have some additional information, video content and things like that. And please get in touch with any questions on scholar one. Gateway back to you Dave.
Thank you Samantha. Before we move on to our next presenter, I just want to remind all the participants to please put any questions you may have in the Q&A portion of Zoom, located at the bottom of your screen. Up next is our next presenter, Richard Bennett, chief growth officer at humm. Thanks, Dave.
Let me move that forward. As Dave said, I'm the chief growth officer for humm. For those of you who don't know. Hum hum is a technology company that has grown up developing products for content rich organizations like publishers. Scholarly publishers are definitely the core of the asset. One of the things that we're going to talk about today is, as we were developing products for these content rich organizations, we found that we were able to utilize AI to bring and apply and extract a significant amount of intelligence from manuscripts.
So part of the work that we've been doing with the kind of behavioral data capture also then led us to be able to look and investigate into products that could be utilized in more of an editorial workflow perspective. So what I'm going to talk to you today is alchemist is a series of products, but the version that we're going to talk about today is alchemist review.
And alchemist review is really applying AI to manuscript intelligence. And so managing manuscript understanding it is a editorial assistant. So it is designed so you'll see a lot of AI tools have been designed to do a specific task on a manuscript. There's a massive proliferation at the moment with various different AI tools doing different things. Alchemist reviews takes a slightly different view as far as we're trying to assist the editor in a specific role and a specific function.
So in this case, it's focused on the first editorial triage step. So that first review. So it's categorizing around a couple of different areas. So the first one is looking to see if we can use AI to offload either routine or impossible. I mean that might be impossible because it's impossible for an editorial person to be able to do it. Or it might be impossible due to workload and time. It's just not the time to be able to do it.
So offloading impossible tasks to AI, the other one is looking at being able to extract understanding, bringing together, summarizing key information from manuscripts. So allowing editors to really be able to focus on the key aspects, the critical human assessment, that is the part that really they really bring to the process. And the last one is really can we apply this and bring some efficiency to the process.
So we're really driving to see if we can actually start being allowing editors to be able to do twice the number of manuscripts with in the same period. So those are the kind of key aspects that we're driving upon for this. So what it is so, so in the first instance it is a private instance.
Everything is sitting within a private cloud. So alchemist review sits as a single instance instance for every publisher. That also means that both the manuscripts are securely held and do not flow outside of the private instance, so they're not being sent out to public models. All of the. It also means that because we're creating a private cloud, we are also able to use the full text of the manuscript, together with prompt engineering, to be able to extract intelligence and information from the manuscript itself.
It's made up of three different sections. So the first one is the manuscript. So a textual analysis. So this is the extraction and analysis and summation of different aspects of the text within a manuscript. The second part is a partnership. We do a partnership with the friends at grounded AI. And they have a product called veracity. So we ensure and we run veracity across all of the references within the paper.
And I'll go into these in more detail in the next slides. And that allows you to be able to do a lot of contextual and metadata extraction and analysis on all of the references within the paper. Again, something that you probably will not be able to get to as an editor to be able to go through 50, 60 or 100 references within the paper itself. The last part.
I won't show you this. I won't go into it in more detail. But we do build in an AI chatbot. So this is really starting to look at whether editors can inquire and have conversations with manuscript in a slightly different way. So the full manuscript is loaded into the model. An editor can be able to ask questions of the manuscript via chat, and just give me all of the aspects within the paper that are referenced, this methodology, and it will return that it also has an external source.
So you can query OpenScholar on an external site. So in the same environment, an editor can sit there and be able to query what are the other published papers that mention this, this term, and/or this methodology. So it gives you an ability to be able to query both internally within the manuscript and externally. If we go a little bit deeper. So the manuscript analysis is essentially three different things.
So the first one is the content extraction and summarization. So we did a piece of work with Oxford University Press called distilled knowledge, and this was to be able to essentially distill a full publication, a full manuscript, into the most entity dense rendering of that that content. It was meant to be used for different purposes, but it also serves the purpose as our expert summary. So this is a 7 over 8 sentence summary that combines things like it combines aspects of the abstract, but also it comes about methodologies, the conclusions and the author claims.
So it's bringing all together in one. We also do the extraction of the author claims the author claims. So these what the authors are claiming are new novel significant from their research. We do extraction of the key concepts. So the key named entities in the paper. And then we're extracting and the key research methods. So how is the research being performed together with the contextual application of that research.
And again, all of that is meant to be a snapshot summary that allows an editor to be able to understand the paper significantly from the first glance. Plants on top of that, you've started to bring in some of the more deep thinking models. And so as the models advance the capabilities and the capabilities, certainly at certain costs become more accessible. So you can start doing things like kind of statistical novelty assessments, so you can start bringing some of that.
You look at writing quality. There's we use some of the visual language models to be able to start bringing in image analysis. So not image analysis like an image twin as far as Western blot or kind of fraudulent in there. But it will analyze things like contextual. So is the context and the way that the figures have been cited actually congruent with what's in the figures themselves.
So those kind of aspects of the, the paper and then you've got things like methodological issues so it can make an assessment of are there any weaknesses in the paper and make a recommendation of any key remedies that you may want to run. The final part, because we're, we have a lot of custom structural data associated with content, you can actually start doing a number of different things.
So you can if we've already created a taxonomy, which we do for our behavioral side, we can be able to apply the taxonomic terms at submission. We can classify the paper type according to the paper types of the journal. We can also start developing. And this is the kind of secondary bit that we're doing right now is looking at retrieval of related content.
So you have a digest but you also can be able to can be able to sorry, my phone's ringing on the other side to be able to have relate to the most related published content relating to that manuscript itself. Dipping down into the citation side. So the guys that grounded do a number of different things. So the first is to do an authoritative check of all of the references within the paper.
So does it exist. Can they find the cited property. Is it can they find the Doi links and return that back to. So that's the kind of health checks. Then it goes kind of further. It goes into things like, you know, are you citing retracted materials. Are you going to have more materials that you're going to be able to reference that are inaccurate or self citations.
And then we've got kind of a full citation meta list. So that gives you the idea of being able to present citation stacking issues, any journal overrepresentation within the tool itself quickly going to so it's in live development. It was developed alongside aps, IOP publishing and aip publishing. It's in live submission use. At this moment in time, we're using their editors to be able to understand the outputs and gain a validation of whether we're creating the output or creating outputs that are both valuable and accurate.
OHP has just launched, and then we've got American Society of Microbiology and AI, which are also launching in August. We're always looking for further development partners to come on board to help us further validate the work that we're doing with this tool. I won't skip into I'll skip into a little bit of this, but one of the things that seems to be coming out is that there are certain types of journals that this seems to benefit more than others.
We're starting to see things with high submission external editors, broader scope journals are seeing greater value. The guys that are very high touch the journals that are very high touch with significant internal resources, maybe less so because they've got more time to be able to assess the manuscripts that are coming into it. So we're starting to see a slightly some sweet spots of where this can be applied at this moment in time.
Last things just going to show. Obviously, one of the things that's really interesting is once you have validation on the outputs and you have validation on the information that's coming out from this, you can do a number of different things. And one of the things that we're looking at is journal fit. And so you can actually start looking at submissions as they're coming in and categorizing them based on a level of four criteria.
So one is relevance to scope. The second is significance and impact, the third originality and novelty and the fourth scientific rigor. And you can actually then start looking at processes, whether that's transfer or whether you want to be able to fast track manuscripts, or you use this as a way of being able to flag things for. Right this isn't really going to fit for us. So we need to get this out of the system as quickly as possible.
That's coming off the back of all of the outputs that we're creating for alchemist review. But it's also you can use it in a number of different ways. And the final aspect that I was going to just flag up is then the related papers. So we're using the full text Association starting to be able to go out. So you as an editor can be able to start going looking not just at the contextual aspects of the extraction of that manuscript, but looking at it in the context of the overall art of the.
An understanding of the discipline. So I think that is the end. If you want to know more, if you want to come on board, if you want to talk to me about potentially coming on as a foundation partner, more than happy to have those conversations. And I shall pass it back to Dave. Thank you Richard. Our next presenter is Eric Olson, co-founder and CEO of consensus.
Awesome Thank you, Dave. Thank you to the SSP team for having us here again. Excited to be chatting with you all today. As Dave said, my name is Eric Olson. I am the co-founder and CEO of consensus. If you don't know what consensus is, and I encourage everybody to scan that QR code there, I'll take you directly into the product.
Or check us out at consensus app. You can sign up for a free account and use it them today. And I think hopefully the presentation will all make a little more sense. If folks are poking around with the product and getting an understanding of what we are. To give you just a sentence on that before I jump in. We are an AI search engine for academic research. We use that terminology in describing what we are pretty intentionally.
We are a search engine first that aids in the discovery of literature. We just use AI in ways to help augment that process and make it a little more efficient. But importantly, we are not a chatbot or an AI lab. We are an Academic Search engine that happens to use AI. Today for the presentation, I'll be doing two three things at the same time.
So first, just talking a little bit about market as we see it today and what we see change that's underway. I'll then intro a little bit more on consensus and how we think we fit into the market. And then I'll conclude with how we're working with publishers and for publishers that are on this call. The opportunity to potentially partner and what that's looking like with other folks and how we want to be a part of this community.
So three, three parts, try to get it done here in 10 ish minutes. We'll see how I do. So first very simple graph and a graph I think that will not be surprised to anyone on this call. This is the weekly active users of ChatGPT. It's estimated that it's about $800 million now today, which means that about 10% of the world is using ChatGPT once a week.
And then if you look on the right, these are some of the markets and users that we all care about on this call. That's where we're seeing its pervasive use growing in some of the highest rates. And it truly is everywhere to an extent I don't think anybody really could have ever imagined just a few years ago. What I think is really interesting.
And for us and for folks on this call that the 800 pounds gorilla in our space of Google Scholar being the primary place where people discover literature, or for now, the first time seeing that traffic start to decline. And it's a combination of that slide, I just showed that people are just using general purpose AI tools for discovery. And a little bit of us and some of our contemporaries, which I'll show here in a second, that are starting to take some traffic from the legacy Academic Search products.
But I think this the second bullet on the right there is what's really important is a lot of the folks that this is happening with are the younger generation folks, the new early career researchers. And that means that this slide that we're looking at today is just starting, and that if these folks grow up in a world where AI I native there'll be a generation of researchers who grow up using tools that are not traditional Academic Search tools.
So when we, you know, at consensus, we talk to lots and lots of end users, view that as part of our core responsibility of building the consumer product. As we're talking to dozens of users a week, thousands of users a year. And when we talk to users about what is most important in their Academic Search tools, we really hear these five things.
So it being having a comprehensive index to search over, having that index in their tools, be able to see the fullness of the content. So full text indexing the second column. Number three, having purpose built features that help with the academic literature review process. So citation generators, saving papers, things we're used to in academic tools.
Then you hear these two things. Number four and five. And these are new entrants to this grid. They want their tools to have AI that is accurate, and AI that is pointed at the content in a way that actually helps them save time and is done in a thoughtful way. It's kind of bucketing that together as effective, accurate, and time saving. And then on the right, you have number five.
This is the new expectation in AI products today. Your UX is going to be AI native. It's going to be flexible. It's going to be delightful. And it's going to be intuitive. And when you look across all these slides or all these rows and you see which tools are growing substantially, 2025 I think it becomes clear that these columns one, two, and three, these traditional academic product requirements are being willing to be compromised by users if products don't bring with them columns four and five.
So if a product doesn't have effective AI or delightful UX, users are willing to compromise on some of these traditional academic features. I think that's a scary thought for everyone on this call, including myself, including society as a whole, that science can suffer if folks are using tools that aren't designed for research just because they help save them time, and just because they have this delightful UX.
So where does consensus fit into this, and how do we see ourselves in the market again. We are an AI native search engine. We are not a chatbot. We are not an AI lab. We are not a general purpose tool. We are a tool that is built for academic research that happens to use AI in it. And we like to use the Google Scholar analogy at consensus.
We know Google Scholar has its set of issues that we hope to improve upon, but it is a good framing of, and it is what we hear from users of what we typically replace in their tool set when we say, hey, what are you using consensus today. What are you used to use. Most common answer just is Google Scholar, and it's a good way to frame that. We are a discovery engine.
We are not a general purpose AI chatbot. And to really highlight that that point home, we have completely different set of actions that are taken in our product compared to general purpose chatbots. So 50% of searches and consensus result in a user leaving our product going to the full text of an article.
So going off platform and going to a publisher's website to access the full text, the click through rate on citations in ChatGPT and perplexity is top 1% So about 100 times more. And we view that as a success. We view that as when a user leaves our platform because they want to go access the full text. That is us doing our job. When we look at retention metrics in our product, we look at what users continue to come back time and time again.
The single most predictive action of retention is a user actually leaving our platform and going to the full text for comment. So in this title of the slide, it is not that is not pulling from thin air. We truly do have a shared interest with publishers that the more we drive them to your sites, the better our product functions and the more people come back to it.
And what we are lucky to say is that we have captured some of this genie. You know, some of this magic spark that AI tools are seeing significant growth. This number is a little outdated. This is from the spring. We're actually doing about now 5 million total visits per month. That consensus, if you look on of how that compares to the landscape, we are past web of science earlier this year in total traffic, and we are hot on the heels of both Scopus and Semantic Scholar, and on track.
By the end of the year to be the second most used cross-disciplinary scholarly search tool in the world. And we're very proud of this and want to continue to set our sights on Google Scholar to be the place that users go to discover and engage with literature. So as we kind of now move into how we're working with publishers and where consensus fits in, this is that same grid I showed before with now consensus and, you know, this is obviously a self-serving take, but we think we have a unique opportunity to be the first tool that checks all of these boxes.
But obviously it takes collaboration with the public and community to do so. We want to have the fullness of content in our product to help better serve our users, so we can be the place that the next generation of users who are demanding AI products, where they're now actually going to Tools that are built for academic research, that they're not going to Tools like ChatGPT, they're not driving you all traffic and that are trying to just train their models on your content.
We want them to continue to go to places where discovery is at the heart of what we're doing, we think we can beat that tool. So moving on to the last part of the presentation on how publishers are working with us today. And what I mean by what I just said, that we want to work with publishers in a similar vein the Google Scholar did 20 years ago, but hopefully advancing your interests even more and being a better, more active partner.
So those partnerships being where the full text is able to be used in the search index for ranking and retrieval with no display. So what this means is we can more accurately surface your content to the right users. But we are still abiding by entitlements and not showing the users the full text, that we are still directing them to your sites to either enter their credentials or make that purchase.
Yeah, I think it's worth saying we have engaged with a number of the big six publishers on agreements just like this, and a number of folks, smaller folks as well. And with some of the partnerships we're about to be announcing here over the next month or two, over 70% of the search results that we will surface will either be fully open access or will be under one of these new partnerships, meaning 70% of our content will be using the full text to retrieve it and rank it for our users.
And if we're not partnering, that means the content is likely not doing as well in being surfaced to the right users. And so what I see my number in on these slides got messed up because it's all point number one. But what is in it for researcher. What is in it for publishers and why publishers are partnering with us. Kind of touched on it in multiple ways, but we view it as four key points.
So number one it is hopefully increased traffic. The better we can surface the content to the right users, the more users will be spilling onto your platforms, either to register usage events for you if they have credentials or purchase the purchase, the papers if they don't. And you know, our product has pretty wide reach to even folks who probably folks who are more novice in their research journeys and many who don't have credentials, who are potentially new entrants to the market, who could be new bits of business for publishers.
The second point support for your core business, institutional customers. We are reached out on a daily basis by University libraries wanting to use our tool and subscribe to our tool for their students. And we get asked by all. What part. What publishers are we working with. Because they want to know if the content that they're paying for exists in our product.
And it's a way that we can be and make this whole ecosystem a little more whole by elevating your content for your customers so that they get more use out of it and hopefully retain as your customers and potentially grow as well. The ones that are not your customers. Number three, we know that this is an exploratory process, and this is new grounds that we're all still figuring out what is the right model for companies like ours to work with publishers.
And we want to give folks room to experiment and control the different levels of visibility in the product. If you look on the right, we have some mockups of what we can show for some of our partner partner publishers and how their content can be surfaced in our product. And we want to experiment with different ways. What does drive more traffic. What does the right amount of a snippet to potentially show look like.
What does different ways we can elevate it in the UX look like. If we want to track the metrics of how people are engaging with it, given a showing up in a search result, and we want to give you the opportunity to experiment with us. And the last one I think is also important is kind of encompassed in what I just said. But this is new, fertile ground, and we want to bring publishers in with us to be partners where we can all learn about what the right way to proceed in this world is.
And if we don't act and we don't work together, we have the same fears that all the air in the room will be sucked up by general purpose chatbots and AI labs like ChatGPT. And that isn't good for science in the world. It also isn't good for the businesses of everybody on this call. It's the last slide I showed. I know, I know.
We are at my 10 minute mark. Summarization of all of how we hope to be active partners to publishers and what we can provide. Google Scholar never did, never did back in the day. We want to be an active part of this community, and we want to work together to preserve the what is amazing about scholarly content world, and what we can enable for scientists and researchers across the world.
I don't have a wrap up slide, but Oh Yeah, we're moving. Moving to Q&A. Thank you all for taking the time to listen. Please do check us out at consensus. If you haven't tried the product, you can sign up for a free account today. Thank you. Eric Yeah. Now we have time for some questions. We're about nine minutes out.
Again, Thank you to DCL silver chair and consensus. And we have a few questions here. So let's start off in the Q&A. What is the business model for consensus. Who pays for it. Yeah so we charge and end users for a subscription. And we charge about $10 per month per user. So our service is the searching and the analysis that we do and present to the user and the interface.
And we charge a small software subscription fee to that. And then if we work with institutional customers, it's a similar thing where we're pricing by users accessing obviously for universities and institutional customers, we do some pretty custom pricing based on FTE, but it's a basic software as a service business model. Thank you. Next one as scholar one. Gateway connects multiple publishers.
What visibility, if any, do individual publishers have into data or submission activity that does not relate to their own journals. Thanks for the question, Sarah. Scholar one. Gateway connects all journals for a given publisher. Making a publisher hub for each individual client of scholar one. So it doesn't go across multiple publishers at this point.
And publishers have access to all of the data that they have today and even greater engagement usage as gateway expands. Great Thank you. So bear with me one second here. So let's move on to this is for DCL. What should what should current textiles customers do to prepare for a transition to content crystallizer in the next year.
I think the biggest thing that they have to do is just start deciding. David, can you speak up a little bit. It's a little quiet. Yeah I'm sorry. Is this better. Yeah my microphone. My mouth. So I think what most people need to do on the front end is really try to establish, how long do they want to try to continue using the extyles tool.
Number two, I think that they need to consider, do they want to keep doing this based on their volume. Some of the customers we've talked to, it actually makes more sense for them to move to a services model. And then number three, I think it's exploring options with some of the providers that are out there that are doing similar things to, to DCL. We'd be happy to talk to anybody about, what they're doing and their process.
It's a robust setup process that we can do with you, and we can really turn this into something that's customized at a high level for your team. Thank you. This one related to ScholarOne. What about gateway will help increase the pool of reviewers reviewer retention. That's a great question. Finding reviewers and getting them to come back and review again, is consistently one of the biggest problems that we hear from our publishing customers.
Gateway is really focused on creating a streamlined and enjoyable author experience that does include reviewer work that those authors are doing, but we have plans in the roadmap to kind of turn directly to the reviewer experience and try to create ways to make that more engaging and easy to use. So there are a few ideas kind of floating around in our roadmap, which we should be able to socialize and start talking about in the coming months.
Great Thank you. And I love the tagline bringing joy to publishing. I had to write that down. Yeah, everybody gets joy somehow. Moving on. One for you, Richard. What KPIs are you tracking to determine editorial office success. Tat volume of submission reviewed or other and any unexpected benefits now that it's live for ASM and others.
Hey, it's a really good question. So the key there's the KPIs tend to break down into two different buckets. There's the qualitative. So can we allow more time for the editors to be able to spend time in critical review and make a better process of review. And that's more qualitative and essentially through surveys and feedback that we're doing directly with the editors, and then the key KPIs it has to make the process more efficient.
I mean, that's essentially what it does. So that is looking at time it takes for an editor to process one manuscript. I mean, initial kind of view from some of the editors that we're working with potential 10% to 15% efficiency, which could easily be significant if you scale that up over time. So I think those are the aspects that we're finding. As far as the KPIs and, you know, so that we're looking at, I think unexpected, maybe unexpected, I mean, I think certainly think that the journal fit to be able to focus on the larger, broader, more significant journals.
Maybe not surprising, but it was interesting to see that reflected back. So one of the big journals that we're testing with is IEEE access, which is a monster of a journal. So that's going to be a real kind of test case where we're going to be testing out exactly how efficient this might be at a scale which which will give us a significant amount of data. So, so, but, but ultimately, we need to focus on making the process more efficient and of a higher quality if potentially possible.
Thank you. And just for the recording and people reviewing it, that is turnaround time. So just for everybody to know Eric one for you a couple for you. But let's start off with how I think you kind of touched on it, but how does consensus compare to or compete with web of Sciences research assistant.
Yeah you know, I think a lot of the AI assisted search tools have some commonalities. And like the overarching way that they're designed and built a search is done, you find papers and there's some AI summary over it. I think that in that like broad strokes view, there's a lot of similarities. Our view at consensus is that the devil really is in the details for consumer grade products, and we pride ourselves on the way that we present the information and the interface as being best in class and hopefully sleek and intuitive, both in the analysis section and the way that we show lots of different formats.
You can view the results in a table, you can have some visual components where you can view the research pretty visually, and then also in the set of search results, the way to go through them. We have our own bespoke UX and interface for that as well, of how to be scanning and interacting with the papers. And I think that all really adds up. And then on top of that, you know, we're still just dipping our toe in this.
But so if I just kind of framed it as like that as the way we display back the results in the bespoke way, the actions that you can take following a set of results, we're really hoping to continue to build out. So we want to build some reference management into the product where you can organize all your collections in a really robust way. And tie that back into the search and analyzing experience. I think there's just infinite run, you know, ground to run on, to continue to build out these products and make them different from one another over time, even if in the most broad strokes way, they're fairly similar.
And then the last thing I'd say is we're, you know, we have really a very comprehensive index, even if some of it is just metadata and abstracts. And we build our own bespoke search as well. So we store all the content that we have in our database and build our own search models. And I think we are pretty best in class. And the accuracy of the retrieval. So given a query how are you finding the right papers.
We really pride ourselves on doing that well and being very flexible to the user. So we can handle Boolean searching and author searching all the way to a paragraph of a conversational prompt. We want to be able to find the most relevant and high quality papers possible for a given search, and we think we do that pretty well. Thank you.
Eric just two more. The question is, did DCL say that styles, spellings, et cetera can be customized for each publisher? If a publisher has multiple journals, can different styles be applied across journals. Yes I did say that we can customize those things, and yes, they can be customized individually by publication. Does not have to be just all one type for a publisher.
Yep Thank you. One last more of an observation and a question. Hum so you had a saying saying I analysis expert judge judges. And so I think that really kind of ties in a number of the presentations here that, you know, I, you know, clearly is the future, but it's going to need some human interaction. So just a quick just two, you know, two seconds from Richard and from Eric on that.
Yeah, I think the, the human part is never going to go away. But what we can do is lift a lot of the inefficiencies and the busy work that needs to be associated with that. And AI is incredibly good at doing that. And as long as we keep the expertise and we enhance the expertise of the human side, I think it will be a success. Yeah, I, I could not could not agree more. I think science and discovery and innovation is pretty inherently a human thing.
And we don't want to take humans out of that. I think you've probably seen on social media or the news that there are products and companies that are claiming they want to build the AI automated scientist, and that is very much not the ambition of consensus. We hope to we hope to get people to understanding what the state of the knowledge is as fast as possible, so they can then do the inherently human thing, just make those new discoveries.
I think there's a long technological discussion. We could also have that. It's it is a limitation of language models today that they're amazing at processing what they have. They're really not good at connecting dots and coming up with new things. I think that's kind of a beautiful thing. Like that leaves that space for humans. That's what we want.
It's going to be it's going to be intensive. Yeah Thank you Eric. All right. So we're at the end here. We actually want a couple of minutes over. Thank you all for staying on. We certainly want to Thank our panelists and all of you for joining us today.
We want your feedback. So please consider completing a session evaluation form. As a reminder, a recording of today's session will be posted on demand library in a few days. And this concludes our session today. So thank you all for participating. Have a good day.
Segment:0 .
All right people are starting to come in.
We're going to give it just about a minute before we get started. If you have a moment and you're inclined, please let us know where you're coming from today. Put it into the chat. Thanks Seattle. Orlando beside Richard.
Let's see who's from the farthest. Oh, we have Oxford. Yeah there you go. That's Richard. Yeah all right. We have. Just about a few more seconds here, and then we'll get started.
All right. Hello and welcome to today's SSP innovation showcase. I'm Dave Myers, CEO of data licensing alliance and a member of the SSP education committee. Before we get started, I have just a few housekeeping items to review. Attendee microphones have been muted automatically. Please use the Q&A feature in Zoom to enter questions for the moderator, myself, and panelists.
You can also use the chat feature to communicate directly with other participants and organizers. Closed captions have been enabled to turn on captions. Select the CC button on your Zoom toolbar. Again, before we get started, a quick note on SSPS code of conduct and today's meeting. We are committed to diversity, equity, and providing an inclusive meeting environment that fosters open dialogue and the free expression of ideas free of harassment, discrimination, and hostile conduct.
We ask all participants, whether speaking or in the chat, to consider and debate relevant viewpoints in an orderly, respectful and fair manner. Now, I'm pleased to introduce our first panelist, David Turner, publishing automation and digital transformation specialist, data conversion laboratory. All right. Howdy, everybody. And Thanks so much to SSP for giving me this opportunity.
As Dave said, I'm David Turner, and I'm a digital transformation consultant and head of the partner relationships at data conversion laboratory. Also known as DCL. I am proud to present our latest solution, which is content crystallizer, which essentially helps a scholarly publication, whether it's a journal article or a book chapter, conference proceeding, or what have you, it helps it to come together, to take shape, to crystallize into a publisher's desired form and format, with the goal of letting you go from word to XML and in 3 easy steps.
So before I get too much further, I do have a quick poll, and I'm not exactly sure how this whole poll thing works, but I think Susan pushes it out there. Yes, we want to know, does the x-files end of life announcement affect you. Yes no. Or what the heck is x-files? The reason that we're asking this is that content crystallizer does a lot of things that are similar to what x-files does.
It actually came about in response to the end of life announcement. So just interested to see what we're working with here. And Susan, I'll just wait for you to post the results whenever you want to say, Yep, this is done.
All right. We got a whole bunch of people that don't know what extyles is. That's great. And others that yes. Have been affected by this. A small percentage of nos. OK, well, let's just dig in. So for those of you who don't know, extyles extyles is a word plugin that's developed by a fantastic person in the industry, Bruce Rosenblum, who sadly passed away a couple years ago.
And it's a tool that's been used successfully in the industry for many different things. Right? auto styling documents, cleaning them up, fixing reference formats and then taking that and converting the content into XML. Bruce's company and sold to add upon a couple of years ago. And just recently, it was announced that they're going to be retiring this tool. So one of our partners came to us and said, hey, do you guys have any kind of tool that could help some of these people out that are looking for help and we don't have any kind of word plug-in, but I, I, I got to looking around and thinking, you know, we actually do have a lot of software in our existing processes.
And you know, and behold, they do a lot of the same things. It's not a word plugin, but it is commercial quality software that we use. And it's not like a single piece of software. It's software that we've used. And, you know, it's not really been configured for external use. So we got to thinking, you know, is there a way that we could do this in a self service manner.
Because obviously we could just tell people, Oh, send it to us, and we'll, we'll do it as an outsourced service. I mean, DCL does that kind of thing all the time. But this is not what that's about. We're looking to try to replicate something where someone has a do it yourself kind of a solution, something that is fully automated, at least from the DCL part of the process, where they can get results in minutes as opposed to, days or hours or whatever it is that we do in another process.
So we came up with this idea of a, a content vending machine, if you will, right? Where the software exists on our servers. You put in your content. Some automation runs, a few minutes later, you get back results. You can make some changes, run it again, get results. So it wasn't a very heavy lift for us because we weren't really creating new software.
We just have been putting existing tools together in a new way, and that's what we've got. And it's resulted in this three step process on this next slide, where basically you come in and you, you, you start by getting the document prepped and that sort of kicks off the crystallization process. Then step two, you let the editor make some fixes, work with the authors to make some edits, and then ultimately load it up again.
And step three then takes you actually from the Word document into XML. So at a little more of a deeper level, the way we started with a Word document, a raw word document, it doesn't have to be styled or anything. It could be a journal article, book, chapter, what have you. But the idea is that the author then loads that as a zip file with a manuscript supplemental file, some basic metadata, and that kicks off this automated doc prep, which does a ton of different things.
Right so it's intended to really start by applying styles automatically to all the different elements of the document. Tables, headings. Author names. Affiliations it goes through and it cleans up spacing problems. Removes blank paragraphs, puts metadata in the right places, checks to make sure the metadata is there. Looks up things on PubMed, Crossref, or peer review system.
It checks citations for completeness, checks the order of citations, identifies unrecognized styles, does all of these things, and then ultimately delivers back a styled word document. And we've tried to match the styles, look and feel also with comments and error reporting integrated into that Word document. So the document is starting to crystallize. It's gone from raw into something that is styled cleaned up and ready to work with.
At this point, the editor then starts to work with it. They incorporate whatever comments they can override elements that they like, they can share back and forth with an author. And when they're done, then they can either run back through auto prep again and have it. Do some other additional checks, or they can move along and they can select the actual conversion. Option and take this thing onto XML.
Now before it does the full XML, the word XML process starts. By doing a check to make sure no errors have been introduced. If if there have been errors that have been introduced, we send back the document with a report. But once it does pass that step, then it moves into an automated conversion process. It converts the document XML. It takes that XML, it parses it against the DTD, and then it runs a series of additional checks, bunch of checks on the XML to make sure that all of these things, all of the styles, everything that the client has set up does match and that they're getting really useful XML.
The whole process takes, you know, minutes. It's, you know, 5 to 10 minutes for part one, it's like 5 to 10 minutes for part two. If anything fails along the way, we send it back to the client with suggestions and comments. But ultimately the result is you get this valid and useful XML. Now, I can't go into all the details in the time that we have here today, but I did go ahead and put together a list.
I violated my PowerPoint rule about amount of text on a slide just so I could help you. Anyway, this is by no means a comprehensive list, but some of the things on the auto doc prep it's doing, it's using AI to auto apply these word styles to all the different elements of the article. It's doing things like verifying that citations have a match. If there's a citation listed at the end, is there a matching citation in the article.
Are the are there other titles where they're supposed to be titles. Do we have the author's name. Do we have the, you know, the affiliations. And then, depending on what the client wants us to do, we can either fix certain things automatically or we can suggest the fix in the comments. Finally, it does things like look for things that are out of place.
So for example, like we found an author name in the middle of a table or an abstract that was like in the complete wrong place in the document or an image floating somewhere. So it does all of those. And then as far as on the XML side, it goes through and it checks like I mentioned before, checks for that suitability first. Then it goes through and it actually creates to make sure that we've got good XML right.
But more importantly, it goes in and it actually checks to make sure we don't just have valid XML, but we have good XML, right. So you could technically have abstract. And then the text insert abstract here and then a close tag of abstract. And that would parse but that wouldn't be useful. XML so we can check all of those things. There are a ton of checks I didn't list here, and we can also customize checks too.
So anyway, I'm pushing up against my 10 minutes. So that's going to take me to my last slide. And if I can actually click the button correctly. While you're scanning the QR code let me just quickly summarize. We provide similar functionality to extyles, but it's not a word plugin like extyles users are used to using. It's a fully automated solution, and it's really geared to help that unstructured word document crystallize into a structured word document, and then that structured word document crystallize into a useful XML.
It can be customized in all sorts of ways how things are ingested, what are the styles are how you want. You know, what automations you want, what checks you want, what XML model you need. You know how you want things reported. And it doesn't just have to be customized at the publisher level. It can also be customized at the publication level.
So if you have a, you know, a couple of journals that you'd like treated different ways we can accommodate that. Or if you have, you know, books that are maybe in different product sets, we can, you know, accomplish that altogether. In any case, that's our presentation. Thanks so much for listening. And Thank you, SSP for this opportunity.
Great Thank you David. Our next presenter is Samantha green, director of product marketing at Silverchair. Thanks so much, Dave. Hi, everyone. As as mentioned, I'm Samantha green. I'm the director of product marketing for Silverchair, and today, I'm really excited to talk to you all about scholar one gateway.
If you were with any of our team at SSP. You may have heard some about this, and I'm excited to get a little bit more into the weeds and talk a little bit more. This is one of our first really tangible steps towards modernizing scholar one, in this case, really focusing on a centralized hub for the author experience. So I thought it would be great to start with just a little bit of a level set on why this modernization matters so much.
And to put it simply, it's because peer review and publishing workflows take around 130,000,000 hours each year, and this is from 2020. The statistic is from 2020. So who knows what kind of inflation has hit this figure in the last five years. And to put that into a different kind of perspective, that's a continuous 15,000 years. So every year and with so much time being spent on these processes, these tools in.
In this technology, the need to create a modern, streamlined experience is really critical. This process takes a lot of time and care, and that time should not be spent wrestling with technology. So to dig a little bit further than that, that statistic, I think in general, the publishing process and publishing infrastructure is a little bit of an unbalanced experience for authors.
Reviews are not evenly distributed. It can be really challenging to conduct reviews in a timely manner. Authors have stated that the submission process can be frustrating and inconsistent across different technologies. The process in general is slow going, and I think publishers have made great strides to shorten that time to publish, but it can still be very complex and challenging to do so.
And in general, submission and peer review platforms have been slow to evolve and can be outdated at times, frankly. Infrastructure like scholar one in general is really big. It's incredibly complex, and it's so important that it's challenging to kind of approach it and changing it in any kind of way that could engage and mitigate risk. So we wanted to look at the process holistically, mapping those pain points and challenges for authors throughout the process, and really think about how we could transform this technology for today's users, but also tomorrow's creating a nice future proofed system.
So our vision for gateway and the future of scholar one more broadly is creating tools that help publishers build community. So in this case, this is an author community. That means giving authors a modern and simple experience that is intuitive and simple. Publishing and peer review, I think, can be rigorous and in-depth without being hard to complete, without those sort of manual tasks being a challenge and adding to the Time and Labor intensive nature of the process.
Building that community also means attracting folks into the platform with configurations and integrations to ease the process. And importantly for publishers who have large portfolios, gateway is a centralized publisher hub with a single view of submissions and reviews across the entire publisher's portfolio. And gateway continues the product development legacy that is so critical to Silverchair and really centers it on community feedback, user feedback, and really leading from what we hear from our publishers and from our users to drive progress.
So our design principles for gateway are pretty simple. Getting published is really exciting for authors, and we want to do what we can to kind of extend that joy to the process. Maybe we can do that by making it more engaging, bringing clarity and transparency to the process and aligning, really aligning the experience of users using scholar one with the same level of UX and technology standards that our users expect, and that they see in every other aspect of their lives with streamlining those processes.
We also bring new efficiencies, new configurations, new integrations that can really extend the reach and the impact of scholar one technologies and scholar one gateway really opens the door to the future of the publishing process. Now, today, gateway is an option across dozens of journals with our development partners. These development partners are publishers who work within the scholar one ecosystem.
And they've been really critical to the creation of gateway. Surfacing real challenges and pain points from their author communities. And in the future, we intend to use this same kind of ethos, these same kind of design and development principles to editors and reviewers to really have gateway serve as the keystone for the modernization of the platform overall. And now I really think it's past time for me to show you what gateway looks like.
So here is the sort of centralized hub itself that you hit when you first log in to gateway. It's highly customizable for publishers, both on the aesthetic level. So you can carry brand colors, fonts, iconography throughout the site, building up that recognition and creating a really customized experience for authors. But you can also customize things beyond the aesthetics, things like author resources, key widgets that can be added throughout the page to create the unique experience that your community needs, especially for things that are more timely or, you know, different types of calls for papers and things like that.
There's a lot of flexibility here. The centralized nature of the homepage also offers really quick access for an author to everything in the publisher portfolio. You can see the find a journal right at the top to help authors orient themselves and get where they're going more quickly. It's really easy to start a new submission, and above the fold there's a dashboard for, you all of the work that's in progress for an author with quick and easy access to continue that work.
Now on to my favorite aspect of gateway. I think it's a great working space for authors. So this is an account view for an author. And from this dashboard they're able to see everything that they need to do, actions required, but also monitoring all other submissions that are in process with other stakeholders in the peer review process. So you can also see this.
This covers both authored articles as well as articles for review, and it's really easy to see the next step for each manuscript. So excuse me, you can see the status of articles at the bottom, awaiting checklists with different peer reviewers and right at the top, you can quickly click into whatever action is needed to continue a submission, whether that's submit or continue your draft.
So as I mentioned, we've built gateway in partnership with publishers so that it will truly benefit all stakeholders. Authors and reviewers, get personalized experiences, easier submissions, and a process that is easier to keep track of. Overall, publishers get really robust user insights that they can use to make changes and directly address new pain points, and they get really powerful integrations to extend their impact and a more customizable brand experience.
And like I mentioned earlier, we're all about bringing joy to this process. But at the bare minimum, creating a place where peer review and editorial management. Technology isn't a pain point and isn't a challenge in the process. And and going from an anchor to a partner will be a really great place for ScholarOne going forward. Ultimately, we see gateway as the first step to transforming the publishing experience in ScholarOne.
Right now, we are expanding the pilot across more journals with our development partners and conducting a series of adoption campaigns to gather more user feedback. We will then obviously use that user feedback to continue to develop gateway and to develop the roadmap beyond gateway. And we're also exploring how to bring some of these same principles of our author focus to editors, to reviewers conducting work along those lines in parallel.
And up next, by the end of the year, we expect to have the plan in place for the full launch of gateway across the scholar one platform. And lastly, you can always see more and learn more by clicking through. Here we have some additional information, video content and things like that. And please get in touch with any questions on scholar one. Gateway back to you Dave.
Thank you Samantha. Before we move on to our next presenter, I just want to remind all the participants to please put any questions you may have in the Q&A portion of Zoom, located at the bottom of your screen. Up next is our next presenter, Richard Bennett, chief growth officer at humm. Thanks, Dave.
Let me move that forward. As Dave said, I'm the chief growth officer for humm. For those of you who don't know. Hum hum is a technology company that has grown up developing products for content rich organizations like publishers. Scholarly publishers are definitely the core of the asset. One of the things that we're going to talk about today is, as we were developing products for these content rich organizations, we found that we were able to utilize AI to bring and apply and extract a significant amount of intelligence from manuscripts.
So part of the work that we've been doing with the kind of behavioral data capture also then led us to be able to look and investigate into products that could be utilized in more of an editorial workflow perspective. So what I'm going to talk to you today is alchemist is a series of products, but the version that we're going to talk about today is alchemist review.
And alchemist review is really applying AI to manuscript intelligence. And so managing manuscript understanding it is a editorial assistant. So it is designed so you'll see a lot of AI tools have been designed to do a specific task on a manuscript. There's a massive proliferation at the moment with various different AI tools doing different things. Alchemist reviews takes a slightly different view as far as we're trying to assist the editor in a specific role and a specific function.
So in this case, it's focused on the first editorial triage step. So that first review. So it's categorizing around a couple of different areas. So the first one is looking to see if we can use AI to offload either routine or impossible. I mean that might be impossible because it's impossible for an editorial person to be able to do it. Or it might be impossible due to workload and time. It's just not the time to be able to do it.
So offloading impossible tasks to AI, the other one is looking at being able to extract understanding, bringing together, summarizing key information from manuscripts. So allowing editors to really be able to focus on the key aspects, the critical human assessment, that is the part that really they really bring to the process. And the last one is really can we apply this and bring some efficiency to the process.
So we're really driving to see if we can actually start being allowing editors to be able to do twice the number of manuscripts with in the same period. So those are the kind of key aspects that we're driving upon for this. So what it is so, so in the first instance it is a private instance.
Everything is sitting within a private cloud. So alchemist review sits as a single instance instance for every publisher. That also means that both the manuscripts are securely held and do not flow outside of the private instance, so they're not being sent out to public models. All of the. It also means that because we're creating a private cloud, we are also able to use the full text of the manuscript, together with prompt engineering, to be able to extract intelligence and information from the manuscript itself.
It's made up of three different sections. So the first one is the manuscript. So a textual analysis. So this is the extraction and analysis and summation of different aspects of the text within a manuscript. The second part is a partnership. We do a partnership with the friends at grounded AI. And they have a product called veracity. So we ensure and we run veracity across all of the references within the paper.
And I'll go into these in more detail in the next slides. And that allows you to be able to do a lot of contextual and metadata extraction and analysis on all of the references within the paper. Again, something that you probably will not be able to get to as an editor to be able to go through 50, 60 or 100 references within the paper itself. The last part.
I won't show you this. I won't go into it in more detail. But we do build in an AI chatbot. So this is really starting to look at whether editors can inquire and have conversations with manuscript in a slightly different way. So the full manuscript is loaded into the model. An editor can be able to ask questions of the manuscript via chat, and just give me all of the aspects within the paper that are referenced, this methodology, and it will return that it also has an external source.
So you can query OpenScholar on an external site. So in the same environment, an editor can sit there and be able to query what are the other published papers that mention this, this term, and/or this methodology. So it gives you an ability to be able to query both internally within the manuscript and externally. If we go a little bit deeper. So the manuscript analysis is essentially three different things.
So the first one is the content extraction and summarization. So we did a piece of work with Oxford University Press called distilled knowledge, and this was to be able to essentially distill a full publication, a full manuscript, into the most entity dense rendering of that that content. It was meant to be used for different purposes, but it also serves the purpose as our expert summary. So this is a 7 over 8 sentence summary that combines things like it combines aspects of the abstract, but also it comes about methodologies, the conclusions and the author claims.
So it's bringing all together in one. We also do the extraction of the author claims the author claims. So these what the authors are claiming are new novel significant from their research. We do extraction of the key concepts. So the key named entities in the paper. And then we're extracting and the key research methods. So how is the research being performed together with the contextual application of that research.
And again, all of that is meant to be a snapshot summary that allows an editor to be able to understand the paper significantly from the first glance. Plants on top of that, you've started to bring in some of the more deep thinking models. And so as the models advance the capabilities and the capabilities, certainly at certain costs become more accessible. So you can start doing things like kind of statistical novelty assessments, so you can start bringing some of that.
You look at writing quality. There's we use some of the visual language models to be able to start bringing in image analysis. So not image analysis like an image twin as far as Western blot or kind of fraudulent in there. But it will analyze things like contextual. So is the context and the way that the figures have been cited actually congruent with what's in the figures themselves.
So those kind of aspects of the, the paper and then you've got things like methodological issues so it can make an assessment of are there any weaknesses in the paper and make a recommendation of any key remedies that you may want to run. The final part, because we're, we have a lot of custom structural data associated with content, you can actually start doing a number of different things.
So you can if we've already created a taxonomy, which we do for our behavioral side, we can be able to apply the taxonomic terms at submission. We can classify the paper type according to the paper types of the journal. We can also start developing. And this is the kind of secondary bit that we're doing right now is looking at retrieval of related content.
So you have a digest but you also can be able to can be able to sorry, my phone's ringing on the other side to be able to have relate to the most related published content relating to that manuscript itself. Dipping down into the citation side. So the guys that grounded do a number of different things. So the first is to do an authoritative check of all of the references within the paper.
So does it exist. Can they find the cited property. Is it can they find the Doi links and return that back to. So that's the kind of health checks. Then it goes kind of further. It goes into things like, you know, are you citing retracted materials. Are you going to have more materials that you're going to be able to reference that are inaccurate or self citations.
And then we've got kind of a full citation meta list. So that gives you the idea of being able to present citation stacking issues, any journal overrepresentation within the tool itself quickly going to so it's in live development. It was developed alongside aps, IOP publishing and aip publishing. It's in live submission use. At this moment in time, we're using their editors to be able to understand the outputs and gain a validation of whether we're creating the output or creating outputs that are both valuable and accurate.
OHP has just launched, and then we've got American Society of Microbiology and AI, which are also launching in August. We're always looking for further development partners to come on board to help us further validate the work that we're doing with this tool. I won't skip into I'll skip into a little bit of this, but one of the things that seems to be coming out is that there are certain types of journals that this seems to benefit more than others.
We're starting to see things with high submission external editors, broader scope journals are seeing greater value. The guys that are very high touch the journals that are very high touch with significant internal resources, maybe less so because they've got more time to be able to assess the manuscripts that are coming into it. So we're starting to see a slightly some sweet spots of where this can be applied at this moment in time.
Last things just going to show. Obviously, one of the things that's really interesting is once you have validation on the outputs and you have validation on the information that's coming out from this, you can do a number of different things. And one of the things that we're looking at is journal fit. And so you can actually start looking at submissions as they're coming in and categorizing them based on a level of four criteria.
So one is relevance to scope. The second is significance and impact, the third originality and novelty and the fourth scientific rigor. And you can actually then start looking at processes, whether that's transfer or whether you want to be able to fast track manuscripts, or you use this as a way of being able to flag things for. Right this isn't really going to fit for us. So we need to get this out of the system as quickly as possible.
That's coming off the back of all of the outputs that we're creating for alchemist review. But it's also you can use it in a number of different ways. And the final aspect that I was going to just flag up is then the related papers. So we're using the full text Association starting to be able to go out. So you as an editor can be able to start going looking not just at the contextual aspects of the extraction of that manuscript, but looking at it in the context of the overall art of the.
An understanding of the discipline. So I think that is the end. If you want to know more, if you want to come on board, if you want to talk to me about potentially coming on as a foundation partner, more than happy to have those conversations. And I shall pass it back to Dave. Thank you Richard. Our next presenter is Eric Olson, co-founder and CEO of consensus.
Awesome Thank you, Dave. Thank you to the SSP team for having us here again. Excited to be chatting with you all today. As Dave said, my name is Eric Olson. I am the co-founder and CEO of consensus. If you don't know what consensus is, and I encourage everybody to scan that QR code there, I'll take you directly into the product.
Or check us out at consensus app. You can sign up for a free account and use it them today. And I think hopefully the presentation will all make a little more sense. If folks are poking around with the product and getting an understanding of what we are. To give you just a sentence on that before I jump in. We are an AI search engine for academic research. We use that terminology in describing what we are pretty intentionally.
We are a search engine first that aids in the discovery of literature. We just use AI in ways to help augment that process and make it a little more efficient. But importantly, we are not a chatbot or an AI lab. We are an Academic Search engine that happens to use AI. Today for the presentation, I'll be doing two three things at the same time.
So first, just talking a little bit about market as we see it today and what we see change that's underway. I'll then intro a little bit more on consensus and how we think we fit into the market. And then I'll conclude with how we're working with publishers and for publishers that are on this call. The opportunity to potentially partner and what that's looking like with other folks and how we want to be a part of this community.
So three, three parts, try to get it done here in 10 ish minutes. We'll see how I do. So first very simple graph and a graph I think that will not be surprised to anyone on this call. This is the weekly active users of ChatGPT. It's estimated that it's about $800 million now today, which means that about 10% of the world is using ChatGPT once a week.
And then if you look on the right, these are some of the markets and users that we all care about on this call. That's where we're seeing its pervasive use growing in some of the highest rates. And it truly is everywhere to an extent I don't think anybody really could have ever imagined just a few years ago. What I think is really interesting.
And for us and for folks on this call that the 800 pounds gorilla in our space of Google Scholar being the primary place where people discover literature, or for now, the first time seeing that traffic start to decline. And it's a combination of that slide, I just showed that people are just using general purpose AI tools for discovery. And a little bit of us and some of our contemporaries, which I'll show here in a second, that are starting to take some traffic from the legacy Academic Search products.
But I think this the second bullet on the right there is what's really important is a lot of the folks that this is happening with are the younger generation folks, the new early career researchers. And that means that this slide that we're looking at today is just starting, and that if these folks grow up in a world where AI I native there'll be a generation of researchers who grow up using tools that are not traditional Academic Search tools.
So when we, you know, at consensus, we talk to lots and lots of end users, view that as part of our core responsibility of building the consumer product. As we're talking to dozens of users a week, thousands of users a year. And when we talk to users about what is most important in their Academic Search tools, we really hear these five things.
So it being having a comprehensive index to search over, having that index in their tools, be able to see the fullness of the content. So full text indexing the second column. Number three, having purpose built features that help with the academic literature review process. So citation generators, saving papers, things we're used to in academic tools.
Then you hear these two things. Number four and five. And these are new entrants to this grid. They want their tools to have AI that is accurate, and AI that is pointed at the content in a way that actually helps them save time and is done in a thoughtful way. It's kind of bucketing that together as effective, accurate, and time saving. And then on the right, you have number five.
This is the new expectation in AI products today. Your UX is going to be AI native. It's going to be flexible. It's going to be delightful. And it's going to be intuitive. And when you look across all these slides or all these rows and you see which tools are growing substantially, 2025 I think it becomes clear that these columns one, two, and three, these traditional academic product requirements are being willing to be compromised by users if products don't bring with them columns four and five.
So if a product doesn't have effective AI or delightful UX, users are willing to compromise on some of these traditional academic features. I think that's a scary thought for everyone on this call, including myself, including society as a whole, that science can suffer if folks are using tools that aren't designed for research just because they help save them time, and just because they have this delightful UX.
So where does consensus fit into this, and how do we see ourselves in the market again. We are an AI native search engine. We are not a chatbot. We are not an AI lab. We are not a general purpose tool. We are a tool that is built for academic research that happens to use AI in it. And we like to use the Google Scholar analogy at consensus.
We know Google Scholar has its set of issues that we hope to improve upon, but it is a good framing of, and it is what we hear from users of what we typically replace in their tool set when we say, hey, what are you using consensus today. What are you used to use. Most common answer just is Google Scholar, and it's a good way to frame that. We are a discovery engine.
We are not a general purpose AI chatbot. And to really highlight that that point home, we have completely different set of actions that are taken in our product compared to general purpose chatbots. So 50% of searches and consensus result in a user leaving our product going to the full text of an article.
So going off platform and going to a publisher's website to access the full text, the click through rate on citations in ChatGPT and perplexity is top 1% So about 100 times more. And we view that as a success. We view that as when a user leaves our platform because they want to go access the full text. That is us doing our job. When we look at retention metrics in our product, we look at what users continue to come back time and time again.
The single most predictive action of retention is a user actually leaving our platform and going to the full text for comment. So in this title of the slide, it is not that is not pulling from thin air. We truly do have a shared interest with publishers that the more we drive them to your sites, the better our product functions and the more people come back to it.
And what we are lucky to say is that we have captured some of this genie. You know, some of this magic spark that AI tools are seeing significant growth. This number is a little outdated. This is from the spring. We're actually doing about now 5 million total visits per month. That consensus, if you look on of how that compares to the landscape, we are past web of science earlier this year in total traffic, and we are hot on the heels of both Scopus and Semantic Scholar, and on track.
By the end of the year to be the second most used cross-disciplinary scholarly search tool in the world. And we're very proud of this and want to continue to set our sights on Google Scholar to be the place that users go to discover and engage with literature. So as we kind of now move into how we're working with publishers and where consensus fits in, this is that same grid I showed before with now consensus and, you know, this is obviously a self-serving take, but we think we have a unique opportunity to be the first tool that checks all of these boxes.
But obviously it takes collaboration with the public and community to do so. We want to have the fullness of content in our product to help better serve our users, so we can be the place that the next generation of users who are demanding AI products, where they're now actually going to Tools that are built for academic research, that they're not going to Tools like ChatGPT, they're not driving you all traffic and that are trying to just train their models on your content.
We want them to continue to go to places where discovery is at the heart of what we're doing, we think we can beat that tool. So moving on to the last part of the presentation on how publishers are working with us today. And what I mean by what I just said, that we want to work with publishers in a similar vein the Google Scholar did 20 years ago, but hopefully advancing your interests even more and being a better, more active partner.
So those partnerships being where the full text is able to be used in the search index for ranking and retrieval with no display. So what this means is we can more accurately surface your content to the right users. But we are still abiding by entitlements and not showing the users the full text, that we are still directing them to your sites to either enter their credentials or make that purchase.
Yeah, I think it's worth saying we have engaged with a number of the big six publishers on agreements just like this, and a number of folks, smaller folks as well. And with some of the partnerships we're about to be announcing here over the next month or two, over 70% of the search results that we will surface will either be fully open access or will be under one of these new partnerships, meaning 70% of our content will be using the full text to retrieve it and rank it for our users.
And if we're not partnering, that means the content is likely not doing as well in being surfaced to the right users. And so what I see my number in on these slides got messed up because it's all point number one. But what is in it for researcher. What is in it for publishers and why publishers are partnering with us. Kind of touched on it in multiple ways, but we view it as four key points.
So number one it is hopefully increased traffic. The better we can surface the content to the right users, the more users will be spilling onto your platforms, either to register usage events for you if they have credentials or purchase the purchase, the papers if they don't. And you know, our product has pretty wide reach to even folks who probably folks who are more novice in their research journeys and many who don't have credentials, who are potentially new entrants to the market, who could be new bits of business for publishers.
The second point support for your core business, institutional customers. We are reached out on a daily basis by University libraries wanting to use our tool and subscribe to our tool for their students. And we get asked by all. What part. What publishers are we working with. Because they want to know if the content that they're paying for exists in our product.
And it's a way that we can be and make this whole ecosystem a little more whole by elevating your content for your customers so that they get more use out of it and hopefully retain as your customers and potentially grow as well. The ones that are not your customers. Number three, we know that this is an exploratory process, and this is new grounds that we're all still figuring out what is the right model for companies like ours to work with publishers.
And we want to give folks room to experiment and control the different levels of visibility in the product. If you look on the right, we have some mockups of what we can show for some of our partner partner publishers and how their content can be surfaced in our product. And we want to experiment with different ways. What does drive more traffic. What does the right amount of a snippet to potentially show look like.
What does different ways we can elevate it in the UX look like. If we want to track the metrics of how people are engaging with it, given a showing up in a search result, and we want to give you the opportunity to experiment with us. And the last one I think is also important is kind of encompassed in what I just said. But this is new, fertile ground, and we want to bring publishers in with us to be partners where we can all learn about what the right way to proceed in this world is.
And if we don't act and we don't work together, we have the same fears that all the air in the room will be sucked up by general purpose chatbots and AI labs like ChatGPT. And that isn't good for science in the world. It also isn't good for the businesses of everybody on this call. It's the last slide I showed. I know, I know.
We are at my 10 minute mark. Summarization of all of how we hope to be active partners to publishers and what we can provide. Google Scholar never did, never did back in the day. We want to be an active part of this community, and we want to work together to preserve the what is amazing about scholarly content world, and what we can enable for scientists and researchers across the world.
I don't have a wrap up slide, but Oh Yeah, we're moving. Moving to Q&A. Thank you all for taking the time to listen. Please do check us out at consensus. If you haven't tried the product, you can sign up for a free account today. Thank you. Eric Yeah. Now we have time for some questions. We're about nine minutes out.
Again, Thank you to DCL silver chair and consensus. And we have a few questions here. So let's start off in the Q&A. What is the business model for consensus. Who pays for it. Yeah so we charge and end users for a subscription. And we charge about $10 per month per user. So our service is the searching and the analysis that we do and present to the user and the interface.
And we charge a small software subscription fee to that. And then if we work with institutional customers, it's a similar thing where we're pricing by users accessing obviously for universities and institutional customers, we do some pretty custom pricing based on FTE, but it's a basic software as a service business model. Thank you. Next one as scholar one. Gateway connects multiple publishers.
What visibility, if any, do individual publishers have into data or submission activity that does not relate to their own journals. Thanks for the question, Sarah. Scholar one. Gateway connects all journals for a given publisher. Making a publisher hub for each individual client of scholar one. So it doesn't go across multiple publishers at this point.
And publishers have access to all of the data that they have today and even greater engagement usage as gateway expands. Great Thank you. So bear with me one second here. So let's move on to this is for DCL. What should what should current textiles customers do to prepare for a transition to content crystallizer in the next year.
I think the biggest thing that they have to do is just start deciding. David, can you speak up a little bit. It's a little quiet. Yeah I'm sorry. Is this better. Yeah my microphone. My mouth. So I think what most people need to do on the front end is really try to establish, how long do they want to try to continue using the extyles tool.
Number two, I think that they need to consider, do they want to keep doing this based on their volume. Some of the customers we've talked to, it actually makes more sense for them to move to a services model. And then number three, I think it's exploring options with some of the providers that are out there that are doing similar things to, to DCL. We'd be happy to talk to anybody about, what they're doing and their process.
It's a robust setup process that we can do with you, and we can really turn this into something that's customized at a high level for your team. Thank you. This one related to ScholarOne. What about gateway will help increase the pool of reviewers reviewer retention. That's a great question. Finding reviewers and getting them to come back and review again, is consistently one of the biggest problems that we hear from our publishing customers.
Gateway is really focused on creating a streamlined and enjoyable author experience that does include reviewer work that those authors are doing, but we have plans in the roadmap to kind of turn directly to the reviewer experience and try to create ways to make that more engaging and easy to use. So there are a few ideas kind of floating around in our roadmap, which we should be able to socialize and start talking about in the coming months.
Great Thank you. And I love the tagline bringing joy to publishing. I had to write that down. Yeah, everybody gets joy somehow. Moving on. One for you, Richard. What KPIs are you tracking to determine editorial office success. Tat volume of submission reviewed or other and any unexpected benefits now that it's live for ASM and others.
Hey, it's a really good question. So the key there's the KPIs tend to break down into two different buckets. There's the qualitative. So can we allow more time for the editors to be able to spend time in critical review and make a better process of review. And that's more qualitative and essentially through surveys and feedback that we're doing directly with the editors, and then the key KPIs it has to make the process more efficient.
I mean, that's essentially what it does. So that is looking at time it takes for an editor to process one manuscript. I mean, initial kind of view from some of the editors that we're working with potential 10% to 15% efficiency, which could easily be significant if you scale that up over time. So I think those are the aspects that we're finding. As far as the KPIs and, you know, so that we're looking at, I think unexpected, maybe unexpected, I mean, I think certainly think that the journal fit to be able to focus on the larger, broader, more significant journals.
Maybe not surprising, but it was interesting to see that reflected back. So one of the big journals that we're testing with is IEEE access, which is a monster of a journal. So that's going to be a real kind of test case where we're going to be testing out exactly how efficient this might be at a scale which which will give us a significant amount of data. So, so, but, but ultimately, we need to focus on making the process more efficient and of a higher quality if potentially possible.
Thank you. And just for the recording and people reviewing it, that is turnaround time. So just for everybody to know Eric one for you a couple for you. But let's start off with how I think you kind of touched on it, but how does consensus compare to or compete with web of Sciences research assistant.
Yeah you know, I think a lot of the AI assisted search tools have some commonalities. And like the overarching way that they're designed and built a search is done, you find papers and there's some AI summary over it. I think that in that like broad strokes view, there's a lot of similarities. Our view at consensus is that the devil really is in the details for consumer grade products, and we pride ourselves on the way that we present the information and the interface as being best in class and hopefully sleek and intuitive, both in the analysis section and the way that we show lots of different formats.
You can view the results in a table, you can have some visual components where you can view the research pretty visually, and then also in the set of search results, the way to go through them. We have our own bespoke UX and interface for that as well, of how to be scanning and interacting with the papers. And I think that all really adds up. And then on top of that, you know, we're still just dipping our toe in this.
But so if I just kind of framed it as like that as the way we display back the results in the bespoke way, the actions that you can take following a set of results, we're really hoping to continue to build out. So we want to build some reference management into the product where you can organize all your collections in a really robust way. And tie that back into the search and analyzing experience. I think there's just infinite run, you know, ground to run on, to continue to build out these products and make them different from one another over time, even if in the most broad strokes way, they're fairly similar.
And then the last thing I'd say is we're, you know, we have really a very comprehensive index, even if some of it is just metadata and abstracts. And we build our own bespoke search as well. So we store all the content that we have in our database and build our own search models. And I think we are pretty best in class. And the accuracy of the retrieval. So given a query how are you finding the right papers.
We really pride ourselves on doing that well and being very flexible to the user. So we can handle Boolean searching and author searching all the way to a paragraph of a conversational prompt. We want to be able to find the most relevant and high quality papers possible for a given search, and we think we do that pretty well. Thank you.
Eric just two more. The question is, did DCL say that styles, spellings, et cetera can be customized for each publisher? If a publisher has multiple journals, can different styles be applied across journals. Yes I did say that we can customize those things, and yes, they can be customized individually by publication. Does not have to be just all one type for a publisher.
Yep Thank you. One last more of an observation and a question. Hum so you had a saying saying I analysis expert judge judges. And so I think that really kind of ties in a number of the presentations here that, you know, I, you know, clearly is the future, but it's going to need some human interaction. So just a quick just two, you know, two seconds from Richard and from Eric on that.
Yeah, I think the, the human part is never going to go away. But what we can do is lift a lot of the inefficiencies and the busy work that needs to be associated with that. And AI is incredibly good at doing that. And as long as we keep the expertise and we enhance the expertise of the human side, I think it will be a success. Yeah, I, I could not could not agree more. I think science and discovery and innovation is pretty inherently a human thing.
And we don't want to take humans out of that. I think you've probably seen on social media or the news that there are products and companies that are claiming they want to build the AI automated scientist, and that is very much not the ambition of consensus. We hope to we hope to get people to understanding what the state of the knowledge is as fast as possible, so they can then do the inherently human thing, just make those new discoveries.
I think there's a long technological discussion. We could also have that. It's it is a limitation of language models today that they're amazing at processing what they have. They're really not good at connecting dots and coming up with new things. I think that's kind of a beautiful thing. Like that leaves that space for humans. That's what we want.
It's going to be it's going to be intensive. Yeah Thank you Eric. All right. So we're at the end here. We actually want a couple of minutes over. Thank you all for staying on. We certainly want to Thank our panelists and all of you for joining us today.
We want your feedback. So please consider completing a session evaluation form. As a reminder, a recording of today's session will be posted on demand library in a few days. And this concludes our session today. So thank you all for participating. Have a good day.
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