Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: Susceptible groupsAnother concept from epidemiology is the identification of “susceptible groups. ” These are population groups that are particularly vulnerable to the effects of health hazards. One example is children. Epidemiologists recognize that children are generally more vulnerable to toxic agents such as lead. This concept is true for indoctrination and incitement as well, as children are particularly susceptible to indoctrination via textbooks, television, youth groups, and messages delivered by authority figures. Burdman has extensively explored the concepts of indoctrination mechanisms used on children [46]. Youth is one factor in susceptibility. Burdman explains how certain cultures may be more susceptible than others. Indoctrination messages are “more readily accepted” by collectivist authoritarian societies where “obedience is the rule” and a “quasi-Pavlovian response” is the norm [47]. The challenge is to find out what reverses such susceptibility. Richter and Stein’s epidemiologic monograph gives further models and examples of this phenomenon [48]. The enduring effects of indoctrination among susceptible populations are noteworthy. Many exposures and effects can be intergenerational when the source of transmission is persistent. This concept is relevant for understanding the contagious spread and multiplying effects of cradle-to-grave exposure to indoctrination and incitement. This principle can be exploited for good or for evil (e. g. , educational systems that promote human rights vs. perpetuating intergenerational transmission of hate). Research exploring neuropsychological structures reveals that the socio-physiological effects of indoctrination and incitement can lead to conditioning, patterning, habituation, and normalization (personal communication of unpublished observations: Trevor Davis). Thus, children raised in educational systems that indoctrinate towards hatred and violence are more likely to pass on such values to their progeny as well. Such intergenerational transmitted ideologies can be extremely resistant to change as they multiply and merge to become part of the core beliefs and values of society. The fallacy of the lone wolfBased on the concepts and epidemiologic models presented, it is clear that the phenomenon of the “lone wolf” is problematic and possibly non-existent. So-called self-radicalization can only occur with prior exposure to the motifs of radical hate ideologies. Lone wolves emerge from populations and sub-groups already susceptible to background exposure. [SEP] Justify:

Output: The text contrasts the claim by emphasizing the limitations of predictability in health issues. It discusses the concept of 'susceptible groups' and how various factors, such as indoctrination and exposure to ideologies, affect vulnerability—pointing out that susceptibility cannot be universally predicted or prevented by any AI. Instead, it illustrates that various influences are at play, suggesting that blanket predictions and preemptive actions are insufficient.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: ” We chose these particular questions over more complicated judgments to minimize the degree to which our findings would be confounded by participants’ statistical sophistication [7,32,41]. For secondary analyses, we probed (a) confidence in each forecast, (b) self-rated expertise on each experiment, (c) additional cognitive variables shown to correlate with forecast skill in other domains: actively openminded thinking [42] and belief in the progress of medical research-adapted from an economic boomster-doomster scale [43], and (d) questions about their familiarity with and the anticipated influence of the RP:CB on how they read papers and design studies. We also asked participants to estimate the overall replication rate the RP:CB will report, but we do not present this data in the current paper since it is premature to do so, and we do not want to confuse researchers’ beliefs about xenograft studies with their beliefs about in vitro studies. Participants were also asked the following: “Imagine a replication study perfectly reproduces the effects observed in an original study. How likely would the effect be as large or larger than that provided in the original study? ” We provided an open text screen for respondents to provide thoughts and feedback on our survey. Further details, including the study protocol, are provided in the (see S1 Text). Surveys were conducted online using Surveymonkey. The order of studies in each survey was drawn randomly for each participant. We analyzed the results of the first 5 studies whose outcomes are resolved and that have been published in eLife [17–21], and we present a sensitivity analysis of the sixth study by describing how the forecast results would change if it replicated the results or not. Accuracy of forecasts were calculated using the Brier score, a proper scoring rule for forecast accuracy that measures the squared deviation between a forecast probability and the actual outcome (coded 0 for nonoccurrence and 1 for occurrence) [44,45], so a lower Brier score denotes a more accurate forecaster than a higher score. A forecaster who behaves like a clairvoyant, correctly classifying all outcomes with complete confidence, would get a perfect Brier score of 0. [SEP] Justify:

Output: The text does not address the claim regarding the predictive abilities of protein properties on their functions. Instead, it discusses the design and analysis of a survey related to replication studies and forecasting accuracy, which does not relate to protein function prediction.


Input: Task: corroborate the claim using the given text. Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: The overall best agronomic results in composted BSG+PD(1:1) are attributed to PD’s ability to modify soil properties, leading to increased water retention and plant uptake of nutrients (Nwachukwu & Ikeadigh, 2012). However, corresponding to the earlier report, this treatment provoked an increased release of plant nutrients, especially N in its available forms, in the soil compared to the rest of the treatments (Ebido & Ndubuaku, 2019). usually promotes vigorous growth of plants via increased photosynthetic activities (Hussain et al. , 2006), and is particularly critical for amaranth production in the tropics (Adeoluwa et al. , 2009; Nyankanga et al. , 2012). The PD enhanced soil total N even more than NPK fertilizers in sandy tropical soils (Umeugokwe et al. , 2021), pointing to the possible contribution of PD to the enhanced N status of BSG+PD(1:1) and the possible role of such status in its performance in the present study. With composted BSG+PD(1:1) as soil amendment, the total biomass yield of amaranth on a fresh-matter basis four weeks after sowing in the first cropping was 46. 51 t ha-1, achieved with 20 t ha-1 being the application rate of the composted BSG-based amendments in this study. From the same study site, the highest amaranth yields of 4. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of composted BSG+PD as a soil amendment, which is a natural fertilizer. It highlights the effectiveness of compost in improving soil properties and promoting plant growth, which aligns with the principles of organic farming that favors natural methods over synthetic chemicals.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: First, the use of censorship by governments raises ethical issues of privacy and free speech in democratic countries [13]. This tension was clearly evident throughout the process of drafting the Convention for the Prevention and Punishment of Genocide [66]. On the one hand, it is clear that governments and public health officials hold an ethical responsibility for their public’s health and to protect the community from harm. Tulchinsky dives into the history and development of community ethics and the challenges posed in balancing individual rights with public health. Failure to implement and enforce public health regulations could constitute negligence on the part of responsible parties [67]. Yet, we must recognize the unavoidable conflict between civil liberties and public security. Second, there are instrumental issues with such measures. These traditional methods have a “limited and specific utility” limiting the spread and effects of specific pieces of incitement, while the influence of indoctrination and ideology remains [35]. Such ideologies are deeply embedded in the mindsets of affected populations. While these motifs may not be explicitly public, they can be triggered by key messages. In the digital age, with the mass dissemination of media messages through time and space, the text and subtext of incitement remains enduring and present in cyberspace. These can be forever retrievable by those programmed to look for them. For example, Twitter attempted to “clean out” ISIS-related incitement by banning hundreds of thousands of accounts. However, new accounts continue to open and many others have simply migrated to other social media sites [68]. Thus, the only way to definitively prevent future violence from the effects of incitement is to increase “audience resistance” [35]. The source of such audience susceptibility and receptiveness needs to be neutralized and countered. There is a need for population-wide measures to replace and counter hate with positive ideologies and to identify and strengthen positive role models. Benesch describes the successful case of a television drama created in Rwanda specifically to develop understanding and resistance to hate speech among the population [35]. The countermeasures proposed by Benesch may be successful in localized scenarios. However, we need to develop countermeasures to meet the challenges of systems of indoctrination that transcend traditional boundaries of space and time. Previous interventions that do not consider indoctrination may be inadequate. The current case studies proposed by Benesch are linked to election outcomes in Africa, triggered by a winner-takes-all election. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of public health measures and the challenges of addressing ideological factors that influence health and safety. It highlights that traditional preventative methods have 'limited and specific utility' and suggests that previous interventions may not be adequate, indicating that predicting and preventing all health issues is not feasible. Therefore, it recognizes significant obstacles that AI would face in fulfilling the claim.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: In contrast, a forecaster who invariably guesses 50%—a common response for participants who do not know how to answer questions about the probability of binary options, sometimes called “the fifty-fifty blip” [46]—would have a Brier score of 0. 25. We report mean Brier scores with bias-corrected 95% bootstrap confidence intervals with 5,000 resamples. All hypothesis tests were conducted using bootstrap t-tests based on whether 95% bootstrap confidence intervals of the mean difference contained the value 0. We also report the extent to which forecasts correctly identified which studies would not replicate using specificity (i. e. , the “true negative rate”), which is normalized by the sum of true negative and false positive forecasts. Data analysis was conducted using R statistical software. Supporting informationS1 Fig. Flow chart of expert sampling process. S2 Fig. Direct comparison of forecasts and Brier scores by rank for survey 1. The first row shows forecasts and the second shows brier scores. The first column shows significance and the second shows effect size. For each panel, the histogram is the aggregate as all of respondents, the red line is for experts and the blue line is for novices. S3 Fig. Likelihood of reproducing upon perfect replication. In the top row, the dark gray histograms show the forecast of a (genericized) perfect replication, and density lines show actual effect size forecasts. The bottom row is the perfect replication ratio: log(forecast/perfect replication). Thus, a negative value indicates the forecast is lower than the perfect replication. The left column shows data from all experts, whereas the right column shows survey 1 data comparing experts to novices. There are multiple interpretations of the idea of a perfect replication. 1) The modal response was. 5, which may indicate that the original effect size is a reasonable estimate of the true effect; 2) some participants thought it should be high (. 95 for students; 1 for novices), perhaps based on the possibility of error from the original study; 3) some participants expected. 8, perhaps based on the power and Type II error from the replication study; 4) and yet others thought it was less than 50% (20% or 30%), suggesting they may have a cynical view of reproducibility in this field. [SEP] Justify:

Output: The text does not address or relate to the claim regarding the predictive capabilities of protein properties for their functions. It discusses statistical methodologies and replication studies without any mention of proteins or their functions.


Input: Task: corroborate the claim using the given text. Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: 30 and 8. 30 t ha-1 were achieved, respectively, with 10 t ha-1 Gliricidia sepium and 60% organic fertilizer (composted market waste plus CD in the ratio of 3:1) complemented with 40% N urine (Adeoluwa et al. , 2006; Adeoluwa et al. , 2009). In northeastern Nigeria, a corresponding amaranth yield of 7. 10 t ha-1 was achieved with PD manure at 20 t ha-1 (Mshelia & Degri, 2014). The much higher yield attained in the present study thus points to the prospects of composted BSG+PD(1:1) in growing amaranth in sandy tropical soils. Generally, mixing BSG with PD manure proved to be superior to mixing BSG with CD manure. However, composted BSG+PD(1:1) could not always find a good substitute in composted BSG+PD(2:1), especially with respect to fresh-matter yields. The overall poor results due to complementation of BSG with CD are attributed to the slow mineralisation rate of CD in coarse-textured tropical soils, about 1. 75 times slower than that of pig-slurry manure (Uzoh et al. , 2015), which in itself usually shows a higher CN ratio than PD (Adubasim et al. , 2018). This observation for CD reflects its low population of bacterial species known to drive its decomposition and hence mineralisation (Oraegbunam et al. , 2023). [SEP] Justify:

Output: The text corroborates the claim by discussing the use of organic fertilizers, specifically mentioning the application of compost and manure (PD manure) to achieve higher agricultural yields. It highlights how the integration of composted market waste with natural fertilizers like PD and BSG contributes to successful organic farming practices.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: They are successful, but may not be sufficient. Here, the stakes are much larger because they are global—as in the case of jihadi totalitarianism. Sustainable strategies for prevention require population-wide strategies for removing the perpetrators of harmful ideology and the sources of incitement. The ideal (but not always practical) preventive strategy is to remove the source by defeating the perpetrators through military defeat. However, as in the case of and Japan, military defeat was not sufficient in and of itself. Defeating the ideology of Nazism required a top-down comprehensive strategy to reeducate the entire population with values and motifs that replaced the old models and motifs, which had led them to support the regime. This was successfully implemented under the in Europe, which included a massive overhaul of societal, economic, and educational programs [46]. Another innovative attempt at developing a countermeasure is Google’s “Redirect Method” to counter ISIS indoctrination and incitement online [69]. The project’s researchers identify vulnerable individuals searching for and viewing ISIS material. These individuals are then gradually and unobtrusively redirected to material that counters ISIS ideology, including Islamic criticism of the religious doctrines as well as evidence countering the public image of the organization’s economic success and popularity. This approach rejects censorship, instead seeking to address the deeper influence of indoctrination. There is an oceanic depth of data available in the modern information age. Could search engines or media platforms be further developed to systematically probe the Internet for deep trends, analyzing what is happening in the sea of ideas, motifs, and messages (“X” [70])? Just as people study sea or air pollution, similar concepts could be employed to probe word pollution. Such methods would not be preventive in nature but could identify trends for the sake of prediction and precaution. Tulchinsky describes the importance of international surveillance networks to monitor such word pollution [67]. Another effective countermeasure is to promote positive deviance and protect role models that embrace tolerance. Reinforcing the status and safety of positive role models is central to a strategy of ideological change [71]. Rather than paying terrorist salaries, what if authority figures supported those who exemplify positive deviance and moderation? There is a strong need for the world to be vigilant to ensure that foreign aid funds are not used to support terror. [SEP] Justify:

Output: The text contrasts the claim by emphasizing that while there are successful AI strategies for prediction, they may not be sufficient for prevention. It discusses various societal and ideological challenges that require broader strategies beyond AI, implying that AI alone cannot predict or prevent all issues. For example, it mentions that 'Such methods would not be preventive in nature but could identify trends for the sake of prediction and precaution,' indicating limitations in AI's capabilities.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: There is a strong peak in panels 3 and 4 for participants who made forecasts in line with a perfect replication. Beyond that, the trend is to make forecasts below a hypothetical perfect replication, so the forecasters were consistently acknowledging that real replication studies are flawed to some degree. Experts were more likely than novices to forecast higher than the perfect replication seemingly suggesting that some of these replication studies were highly likely to replicate the original results compared to other studies. S1 Table. Completions by survey. S2 Table. Forecast descriptive statistics and prediction accuracy by trichotomized predictors. S3 Table. Descriptive statistics of discretized predictors. S4 Table. Original and replication study results. S5 Table. Sensitivity analysis of Brier scores by outcome and identified expertise. S1 Text. Study protocol. S1 Data. Data file. Potentially identifying information has been redacted to protect the anonymity of the participants. S2 Data. Codebook. 1. . Clinical development success rates for investigational drugs. Nat Biotechnol. 2014;32: 40–51. pmid:24406927 View 2. Baker M. 1,500 scientists lift the lid on reproducibility. Nat News. 2016;533: 452. pmid:27225100 View 3. . Believe it or not: how much can we rely on published data on potential drug targets? Drug Discov. 2011;10: 712–712. pmid:21892149 View 4. Bissell M. Reproducibility: The risks of the replication drive. Nat News. 2013;503: 333. View 5. Kaiser J. The cancer test. Science. 2015;348: 1411–1413. pmid:26113698 View 6. Morrison SJ. Time to do something about reproducibility. eLife. 2014;3: e03981. pmid:25493617 View 7. . An open investigation of the reproducibility of cancer biology research. eLife. 2014;3: e04333. pmid:25490932 View 8. . Bringing probability judgments into policy debates via forecasting tournaments. Science. 2017;355: 481–483. pmid:28154049 View 9. -H, Huber J, Johannesson M, et al. [SEP] Justify:

Output: The text does not address the claim regarding the predictive abilities of protein properties related to their functions. Instead, it discusses statistical trends in forecasting and reproducibility in research without linking any of these topics back to protein properties or their functional predictions.


Input: Task: corroborate the claim using the given text. Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: Notably, the highest reductions in exchangeable acidity occurred in composted BSG+PD(1:1). This reduction, which may not be unconnected with its excellent performance in this study, can be attributed to the liberation and loss via leaching of Al3+ chelated with base-forming cations during PD mineralisation in this acidic sandy soil (Garzón et al. , 2011; Tang et al. , 2007). The data further showed nominally higher values of soil organic carbon, total N, available P and base-forming cations (except Mg2+) in composted BSG plus PD than composted BSG plus CD, regardless of mixing ratio (Ebido & Ndubuaku, 2019). Amaranth growth and yield were influenced mostly by the soil content of Ca2+. Calcium is a crucial plant nutrient for the growth and development of plants (McLaughlin & Wimmer, 1999), particularly vegetable crops (Hao & Papadopoulos, 2003; Parra-Terraza et al. , 2008). Soil structure enhancement due to organic amendment mediates increased yields of short-duration vegetables in coarse-textured tropical soils (Ogunezi et al. , 2019). [SEP] Justify:

Output: The text provides evidence that organic amendments, such as compost, contribute to improved soil quality and plant growth, which supports the claim that organic farming relies on natural fertilizers. Specifically, it discusses how composted biomass affects soil properties and influences crop yield, reinforcing the idea of using organic materials over synthetic chemicals.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: Proposing precautionary countermeasures to identify dangerous and hateful ideology aims to advance the locus of detection to earlier stages. Any countermeasures would need to be measured in terms of their validity to determine if they measure what they purport to measure. Emerging issues and limitationsIn this study, we presented only a modest number of examples of indoctrination as an upstream factor to genocide and genocidal terror. We recognize that several limitations apply to our study. As noted above, epidemiology is guided by the basic questions: who, what, where, when, and how. The more difficult question to address may be why indoctrination and incitement exist in the first place. This question is beyond the scope of this paper yet deserves contemplation. This essay is not intended to probe what are the sources of evil or identify what are the sources of goodness. Because we recognize a cause-effect relationship between ideologies and actions, we have to ask what it is in belief systems that result in evil outcomes. We have not examined how religious, ethnic, or racial factors contribute to genocide or genocidal terror. Our premise is that ideology subsumes religion and crosses traditional, racial, and ethnic boundaries. We have not dedicated adequate attention to emerging issues in Europe and the Far East, in particular, the burgeoning Rohingya crisis in Myanmar [72]. Rohingya minority in Myanmar has been subjected to systematic top-down government-organized classification, symbolization, persecution, exclusion from citizenship, threats, atrocities, forced expulsion, and organized killing. A recent report released by Frontieres (MSF) revealed that, according to conservative estimates, 647,000+ Rohingya have been forced to flee into Bangladesh, and 6700+ Rohingya have been killed since August 2017 [73,74,75]. are an example of a Muslim minority that has suffered discrimination, dehumanization, demonization, delegitimization, and genocidal crimes against humanity not resulting from totalitarian jihadist indoctrination or perpetration. Information is not readily available to the degree to which indoctrination and incitement were instrumental in this scenario. There is a need to examine whether our models apply in such settings today. A major limitation of our models on indoctrination and incitement leading genocide is how to account for the governmental campaign of butchery and barbarity in Syria. There does not appear to be a history of incitement against those targeted by unspeakable atrocities and crimes against humanity perpetrated by Assad’s regime. [SEP] Justify:

Output: The text contrasts the claim by discussing issues related to indoctrination and genocide rather than health prediction or prevention. It emphasizes the limitations of models addressing indoctrination and actions, indicating a lack of capability to fully understand or predict complex social issues, which aligns with the idea that AI may not be capable of predicting all health issues.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Evaluating replicability of laboratory experiments in economics. Science. 2016;351: 1433–1436. pmid:26940865 View 10. Using prediction markets to estimate the reproducibility of scientific research. Acad Sci. 2015;112: 15343–15347. pmid:26553988 View 11. Coadministration of a Tumor-Enhances the Efficacy of Cancer Drugs. Science. 2010;328: 1031–1035. pmid:20378772 View 12. -Cordero A, et al. Discovery and of of Expression Data. Med. 2011;3: 96ra77–96ra77. pmid:21849665 View 13. Melanoma genome sequencing reveals frequent PREX2 mutations. Nature. 2012;485: 502–506. pmid:22622578 View 14. BET as a to Target c-Myc. Cell. 2011;146: 904–917. pmid:21889194 View 15. -I, et al. Inhibition of BET recruitment to chromatin as an effective treatment for MLL-fusion leukaemia. Nature. 2011;478: 529–533. pmid:21964340 View 16. Willingham SB, Volkmer J- The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors. Acad Sci. 2012;109: 6662–6667. pmid:22451913 View 17. . Replication Study: BET bromodomain inhibition as a therapeutic strategy to target c-Myc. eLife. 2017;6: e21253. pmid:28100400 View 18. . Replication Study: The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors. eLife. 2017;6: e18173. pmid:28100392 View 19. . Replication Study: Melanoma genome sequencing reveals frequent PREX2 mutations. eLife. 2017;6: e21634. pmid:28100394 View 20. . Replication Study: Discovery and preclinical validation of drug indications using compendia of public gene expression data. eLife. 2017;6: e17044. pmid:28100397 View 21. . [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses various studies related to scientific research topics without addressing the relationship between protein properties and their functions. The lack of direct reference to proteins or the predictive nature of their properties means the text does not support, contrast, or provide evidence related to the claim.


Input: Task: corroborate the claim using the given text. Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: However, with Ca that controlled amaranth yield in this study also being involved in plant absorption of soil nitrate (Habbasha & Ibrahim, 2015) and ammonium (Feagley & Fenn, 1998), leaf fresh-matter yield depending on soil total N here would be understandable. Beyond Ca’s role in N transport, it interacts with P and K, enhancing the absorption of ammonium, P, and K (Acosta-Durán et al. , 2007; Habbasha & Ibrahim, 2015). The excess accumulated N is stored, and its adsorption stimulates photosynthetic processes and increases plant growth and leafiness. This relationship between N and Ca in soils would, therefore, explain the dependence of leaf fresh-matter yield on soil total N content. Indeed, composts have elevated pH and supply N (mostly in the form of nitrates) as well as the base-forming nutrient elements (Rosen & Bierman, 2005; Tiquia, 2003). In this study, composted BSG+PD(1:1) was the best treatment in terms of vegetative growth and yields of amaranth. This is most likely because amaranth production in the tropics depends on the N-fertility of the soil (Adeoluwa et al. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of compost in supplying nitrogen, which is a key aspect of organic farming practices. It highlights that compost not only elevates soil pH but also provides nitrogen mostly in the form of nitrates, which supports plant growth, specifically amaranth in the study mentioned. This aligns with the claim that organic farming utilizes natural fertilizers like compost.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: We acknowledge that our models may not provide explanatory power for the crisis in Syria, in which 470,000+ people have been killed and more than 12 million have become refugees [76]. Our study does not delve into the reemergence of xenophobia and anti-Semitism in Europe, hate crimes in the United States, nor consider neo-nationalist and alt-right movements, all of which pose emerging threats today [77]. These social trends, potentially triggered by undercurrents of racism or the mounting immigration crisis and fear, bear alarming parallels to the development of fascist and Nazi movements in Europe in the 1930s, which made ample use of racist motifs and stereotypes to dehumanize and demonize minority groups. Problems posed by immigration may be real but are increasingly dangerous when they become a pretext for using the 5 Ds. Due to the authoritarian nature of totalitarian regimes and societies, there is limited data regarding “hidden” populations (outliers/positive deviance) that reject the hateful motifs of extremist jihad and yet are silenced due to intimidation, coercion, force, or cultural restrictions. There may be many among these societies that, in fact, favor more moderate ideologies and yet remain anonymous out of fear. In applying the concepts of epidemiology to the study of genocide prevention, we have to be aware of the dangers and potential abuses of proposing population-wide countermeasures to any system of belief or ethos (e. g. , indoctrination of alternative motifs). Such countermeasures need to be weighed in regard to possible dissonance between civil liberties and basic human rights. A potential criticism to our assertions is the idea that these concepts are largely theoretical as opposed to evidence-based. Statements about cause-effect relationships between incitement and violence may be questioned based upon ecologic associations [78]. Whenever there are temporal associations between increases in measures of incitement and increases in measures of actual violence or, conversely, decreases in both, these empirical associations provide plausible evidence for cause-effect relationships. When the population under observation is followed over time, then before-after comparisons control for confounders. There is clear potential for population-wide intervention as demonstrated by the Rose Model. Specific interventions directed at groups rather than individuals (such as text messaging campaigns in Africa, the post-war educational reform in Europe and Japan after II) are examples of population-wide countermeasures that resulted in total change to political systems of indoctrination [79]. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of models in addressing complex social issues, such as the crisis in Syria and rising extremism in Europe. It emphasizes that there are significant societal challenges that may not be adequately addressed by predictive models, particularly highlighting the theoretical nature of proposed countermeasures without empirical evidence to support causation. This implies that not all health issues, or broader social problems, can necessarily be predicted or prevented by AI.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Replication Study: Coadministration of a tumor-penetrating peptide enhances the efficacy of cancer drugs. eLife. 2017;6: e17584. pmid:28100395 View 22. Gelman A, Carlin J. Beyond power calculations assessing type S (sign) and type M (magnitude) errors. Sci. 2014;9: 641–651. pmid:26186114 View 23. Weissman I. Response to: “Replication Study: The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors. ” eLife. 2017;6: e18173. View 24. Baker M, Dolgin E. Cancer reproducibility project releases first results. Nat News. 2017;541: 269. pmid:28102271 View 25. eLife editorial. The challenges of replication. eLife. 2017;6: e23693. pmid:28182866 View 26. Kaiser J. Rigorous replication effort succeeds for just two of five cancer papers. In: Science | AAAS 18 Jan 2017. 27. . Overconfidence in interval estimates: What does expertise buy you? Process. 2008;107: 179–191. View 28. Önkal D, Yates JF, Simga-Mugan C, Öztin Ş. Professional vs. amateur judgment accuracy: The case of foreign exchange rates. Process. 2003;91: 169–185. View 29. Tversky A, Kahneman D. of Base Rates. ResearchGate. 1981; 29. View 30. Kahneman D, Lovallo D. : A on Risk Taking. Manag Sci. 1993;39: 17–31. View 31. . The moderating effects of problem characteristics on experts’ and novices’ judgments. J Mark Res. 1997;34: 233–247. View 32. . Social, Behavioral, and Perspectives on Robust and Reliable Science: Report of the Subcommittee on Replicability in Committee to the for Social, Behavioral, and [Internet]. Foundation; 2015 May. 33. . What does research reproducibility mean? Med. 2016;8: 341ps12–341ps12. pmid:27252173 View 34. . Is psychology suffering from a replication crisis? [SEP] Justify:

Output: The text provided does not address the claim regarding the predictive capabilities of protein properties related to their functions. Instead, it discusses various replication studies and related scientific articles without providing any details or arguments connected to the claim. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: , 2009; Nyankanga et al. , 2012), and this composted BSG+PD(1:1) had the highest total N in the two cropping cycles (Ebido & Ndubuaku, 2019). Also, soil N through the aforestated mechanisms influenced Ca2+ content on which amaranth productivity largely depended. It is thus recommended that BSG be complemented with PD in an equal-volume mix before composting to enhance the bioavailability of N and Ca, and to optimally express the relationship between these two nutrients, thereby increasing the productivity of amaranth in acidic sandy tropical soils. Besides the potential of composted BSG+PD(1:1) for increasing the productivity of amaranth and similar short-duration vegetables in the tropics, its wide adoption could be one way to enhance the utilisation and productive recycling of brewery waste, thereby promoting the concept of circular economy. There are indications that this composted BSG+PD(1:1) can produce higher crop yields than compound mineral fertilizers (Alayu & Leta, 2020; Moyin-Jesu, 2003; Ojeniyi et al. , 2007). Considering the abundance and cheap availability of BSG compared to mineral fertilizers, the adoption of composted BSG+PD(1:1) in crop production can also be more cost-effective. It could also help to reduce the prevailing pressure on PD, sparing it for other purposes such as bioremediation of crude oil-polluted soils (Ezenne et al. [SEP] Justify:

Output: The text corroborates the claim by discussing the benefits of using composted materials like BSG (Brewer's Spent Grain) and PD (Pulp) for enhancing soil nutrients and crop productivity. It highlights that composted BSG+PD can produce higher crop yields than synthetic mineral fertilizers, aligning with the idea that organic farming prefers natural fertilizers over synthetic options.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: Their stories provide excellent empirical evidence for our argument. Such temporal associations are highly suggestive of true cause-effect relationships, even if the analysis is not brought from the macro to the micro. What we are looking for is a change in one group related to a change in another group over time—or, the delta versus the delta over time. The examples presented are theoretical concepts with empirical validity that have explanatory power. Even so, actual epidemiological studies will need to be carried out to provide specific and additional evidence for our assertions, as well as quantify the relationship between variables. Gordon reiterates the premise that there is a cause-effect relationship between hate speech and mass atrocities including genocide and he proposes a unified legal approach to accountability for the consequences, in the context of international criminal law [80]. [SEP] Justify:

Output: The text contrasts the claim by discussing the need for actual epidemiological studies to substantiate theoretical concepts and the cause-effect relationships proposed. It emphasizes that while there may be temporal associations suggestive of relationships, empirical evidence is necessary to validate any claims regarding predictions about health issues, thereby undermining the assertion that AI can predict and prevent all health issues.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: , 2021; Toews, 2021). The vast majority of drug targets are proteins, and structures play a critical role in rational drug design (Anderson, 2003; Kolb et al. , 2009). In particular, rational drug design strategies frequently require determining structures of the target protein bound to many ligands, as information on how these ligands bind is critical both to discovering early-stage drug candidates and to optimizing their properties (Maveyraud and Mourey, 2020; Warren et al. , 2012). Determining these structures experimentally is typically slow and expensive, and sometimes proves impossible (Slabinski et al. , 2007). Tremendous effort has thus gone into development of computational docking methods for predicting ligand-binding modes, which have dramatically accelerated this process in cases where a high-resolution experimentally determined structure of the target protein is available (Ferreira et al. , 2015; Pinzi and Rastelli, 2019). However, the metrics generally used to evaluate protein structure prediction methods—including all of those used in CASP—focus on the overall accuracy of predicted structures, rather than on their utility in predicting ligand binding. This has left the large community of academic and industrial researchers who wish to use protein structures to design drugs and other ligands unsure of whether, when, and how to use the new structural models. Improvements in protein structure prediction are widely assumed to lead to better models of ligand-binding pockets and thus more accurate predictions of ligand binding (Beuming and Sherman, 2012; Bordogna et al. , 2011; Erickson et al. , 2004), but to what extent do these assumptions hold? Here, we address these questions by evaluating (1) the structural accuracy of ligand-binding pockets in modeled proteins and (2) the accuracy of ligand-binding poses predicted using these models, where a ligand’s binding pose is specified by the 3D coordinates of its atoms when bound to the target protein. We evaluate models generated both by AF2 and by a traditional template-based modeling strategy, systematically comparing both to experimentally determined structures. Several recent studies have experimented with the use of models generated by AF2—and related protein structure prediction approaches such as RoseTTAFold (Baek et al. , 2021)—for tasks related to drug design, with mixed results (Díaz-Rovira et al. , 2023; He et al. [SEP] Justify:

Output: The text does not directly address the claim regarding the predictive accuracy of protein properties on their functions. Instead, it discusses computational docking methods and structure prediction for drug design, touching upon ligand-binding predictions rather than directly linking protein properties to functional predictions. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: , 2014), to the benefit of the environment. The soil data showed a slightly higher content of exchangeable Na (Na+) of 0. 15 cmol kg-1 in both composted BSG+PD(1:1) and BSG+PD(2:1) than the rest in the two cropping cycles (Ebido & Ndubuaku, 2019). However, from the perspective of the tendency for microaggregate breakdown and colloidal dispersion in humid tropical soils to increase with an increasing amount of Na+ relative to the entire cations or the other base-forming cations (Igwe & Obalum, 2013), these two treatments of pre-composting complementation of BSG with PD potentially cannot have negative implications for the environment. When its absolute level is considered, the soil Na+ content due to them is far below the critical value of 1. 0 cmol kg-1 above which agricultural soils can experience undesirable sodicity and associated colloidal dispersion, predisposing them to water erosion (Oluwatuyi et al. , 2020). For one-time soil application in agronomic production, the chances of realising residual effects in the soil are generally greater with organic amendments than mineral fertilizers (Ndzeshala et al. , 2023; Unagwu et al. , 2013). However, mineralisation-enhanced organic amendments like composted biowaste and liquid fermentate sometimes do not have residual effects in the soil (Ndzeshala et al. [SEP] Justify:

Output: The text supports the claim by discussing the benefits of organic amendments, particularly compost, in agronomic production. It indicates that the chances of realizing residual effects in the soil are generally greater with organic amendments compared to mineral fertilizers. This implies that organic farming practices, which often include the use of natural fertilizers, are effective for soil enhancement and align with the claim of relying on natural sources instead of synthetic chemicals.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: Skip to Content eLife home page your research Search by keyword or author Reset form my search to is a correction notice. Read the corrected 10. 7554/eLife. 91529 Open access Copyright information Open annotations (there are currently 0 annotations on this page). 145 views Share this 1529. Copy to clipboard Download BibTeX Download. RIS Full text Side by side Main text 2021. Neural signatures of vigilance decrements predict behavioural errors before they occur. eLife 10:e60563. doi: 10. 7554/eLife. 60563. Published 8 April 2021 In the decoding distance to object analyses on correct trials, classifiers were trained and tested using data from across timepoints (since each distance was a bin spanning multiple timepoints) and trials. Signal samples from all trials and timepoints within each distance were randomly allocated to one of the 10 folds to be used in the 10-fold cross-validation process. This meant that, on any given iteration of cross-validation, a single sample was never in both the training and the testing set, but samples in the training and testing set could be drawn from different timepoints of the same experimental trial. Autocorrelation in the data over time could therefore have inflated the decoding accuracy. To correct this, we have re-run the analysis decoding distance to object ensuring that training and testing sets drew from independent trials, and re-ran all analyses that drew on these decoding results. Analyses that did not decode distance to object are not affected, and analyses decoding distance to object on error trials are also not affected. We replicate our main finding of being able to use neural patterns to predict the occurrence of errors, along with all the effects of attention. The interaction indicating decreased distance decoding for late vs early blocks in monitoring but not active conditions has decreased in strength from being evident (BF >3) at two consecutive time-windows to one (BF = 6. 7) and there is no longer a main effect of target frequency. The link we reported between behaviour and informational connectivity is no longer present. This necessitated the changes outlined below. Underline indicates differences between corrected and original text. [Abstract] [Corrected text: #1] There was subtle evidence of this also in the neural decoding using Magnetoencephalography: for one time-window (of 80ms) coding of critical information declined more during monitoring versus active conditions. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of the predictive capabilities of AI in health contexts. While it mentions the ability of signals to predict the occurrence of errors in a neural context, it does not support the broader claim that AI can predict and prevent all health issues. It indicates that there are nuances and challenges involved in predictive analysis, such as the decreased accuracy in certain conditions and changes in data interpretation.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: , 2023; Heo and Feig, 2022; Lee et al. , 2022; Lee et al. , 2023; Liang et al. , 2022; Qiao et al. , 2022; Scardino et al. , 2023; Wong et al. , 2022). Our study stands apart in two regards. First, we use structural models generated without any use of known structures of the target protein. For machine-learning methods, this requires ensuring that no structure of the target protein was used to train the method. Second, we perform a systematic comparison that takes into account the variation between experimentally determined structures of the same protein when bound to different ligands. Our results reveal both strengths and weaknesses of AF2 models. The structural accuracy of ligand-binding pockets in AF2 models is usually substantially higher than that of traditional homology models. Indeed, the typical difference between corresponding binding pockets in an AF2 model and in an experimentally determined structure is only slightly larger than the typical difference between two experimentally determined structures of the same protein with different ligands bound. Surprisingly, however, ligand-binding poses predicted given AF2 models are not significantly more accurate than those predicted given traditional models and are much less accurate than those predicted by docking computationally to experimentally determined protein structures. Our results provide guidelines as to how AF2 models should—and should not—be used for effective ligand-binding prediction. Our findings also suggest opportunities for improving structure prediction methods to maximize their impact on drug discovery. of proteins and models to ensure a fair comparison Evaluating the accuracy of predicted ligand-binding poses requires that we examine protein–ligand complexes whose structures have been determined experimentally. We wish to ensure, however, that the predicted structures of a given protein are not informed by experimentally determined structures of that same protein. AF2 and other recent neural-network-based protein structure prediction methods use experimentally determined protein structures in two ways: first, to train the neural network and second, as templates for prediction of individual protein structures (Baek et al. , 2021; Jumper et al. , 2021). The AF2 neural network was trained on structures in the Bank (PDB) (Berman et al. , 2000) as of April 30, 2018 so we evaluated structural models of proteins for which no experimentally determined structure was available as of that date. [SEP] Justify:

Output: The text discusses the evaluation of structural models and the accuracy of predicted ligand-binding poses but does not directly address the claim about researchers arguing that the properties of proteins can predict their functions accurately. Instead, it comments on the differences in accuracy between various protein structure prediction models without making a connection to functional predictions, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: , 2023; Obalum et al. , 2019). This constraint with organic amendments is particularly the case in the humid tropics due to the prevailing high temperatures, and is true not only for effective manures like PD, but also for those that mineralise rather slowly in the soil like CD (Ezenne et al. , 2019; Obalum et al. , 2020). In the present study, the best-performing composted BSG+PD(1:1) had residual effects, out-yielding all the other treatments (Figure 2). This, which could be caused by amaranth’s short life cycle, offers the farmer the invaluable opportunity of double cropping without compost re-application. Smallholder farmers of short-duration vegetables in coarse-textured and low-fertility soils of the derived savannah and similar tropical locations can take advantage of these findings to produce the increasingly highly sought-after ‘organic’ vegetables for improved farm income and livelihood on a sustainable basis. 5. ConclusionsThe study explored the prospects of complementing BSG with PD and CD before composting for amaranth production in low-fertility sandy tropical soils. With the exception of BSG plus PD at an equal volume ratio (BSG+PD(1:1)), the composted BSG-based organic soil amendments were generally ineffective in increasing amaranth productivity. The BSG was not compatible with the manure components of the compost including poultry droppings (PD), except when mixed with PD in a favourable 1:1 ratio. The differences between composted BSG+PD(1:1) and others were mostly a reflection of their relative abilities to supply Ca and N in the soil. A composted mix of equal volumes of BSG and PD could thus be beneficially explored in overcoming Ca- and N-deficiencies for increased production of amaranth and similar leafy vegetables in low-fertility, sandy tropical soils. Author ContributionsConceptualization, O. O. A. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of composted organic soil amendments, such as BSG (brewer's spent grain) and manure components like poultry droppings (PD), in organic farming. It highlights that these natural amendments can be beneficial for increasing productivity in low-fertility soils, aligning with the idea that organic farming utilizes natural fertilizers.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: This is consistent with a previous study (Alnaes et al. , 2015), which suggested that large-scale functional brain connectivity depends on the attentional load, and might underpin or accompany the decrease in information decoding across the brain in the unattended condition. [Original text: #1] Results showed strong evidence (Bayes factor = 6. 3e21) for higher informational connectivity for trials with Attended compared to Unattended dots, and moderate evidence for higher connectivity in Active compared to Monitoring conditions (Bayes factor = 3. 4; Figure 4B). There was insufficient evidence to determine whether there was a main effect of Time on Task (Bayes factor = 0. 83). There was moderate evidence for no two-way and three- way interactions between the three factors (Bayes factor ANOVA, two-way Time on Task-Target Frequency: BF = 0. 17; Time on Task-Attention: BF = 0. 16; Target Frequency-Attention: BF = 0. 15; their three-way interaction BF = 0. 12). These results suggest that Monitoring conditions and trials in which the dots are in the distractor (Unattended) colour, in which the attentional load is low, result in less informational connectivity between occipital and frontal brain areas compared to Active conditions and Attended trials, respectively. This is consistent with a previous study (Alnæs et al. , 2015), which suggested that large-scale functional brain connectivity depends on the attentional load, and might underpin or accompany the decrease in information decoding across the brain in these conditions. [Corrected figure: Figure 4B,C] [Original figure: Figure 4] [Corrected text: #2] There was moderate evidence for no difference in connectivity on miss compared to correct trials (Bayes factor = 0. 11). In addition, there was moderate evidence for no effect of Time on Task and (BF = 0. 11 and BF = 0. 10, respectively), as well as for two-way and three-way interactions between the three factors (Bayes factor -Target Frequency: BF = 0. 14; Behaviour-Time on Task: BF = 0. 14; Target Frequency-Time on Task: BF = 0. 15; their 3-way interaction BF = 0. 14). [SEP] Justify:

Output: The text contrasts the claim by focusing on the findings related to brain connectivity and attentional load, which do not support the notion that AI can predict or prevent all health issues. Instead, it provides evidence on functional brain connectivity under various conditions and indicates insufficient evidence for certain effects, underscoring the complexity and limitations of predictions in neuroscience contexts.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: In addition, when generating models using AF2, we used only structures that were available on that date as templates (see Methods). For a fair comparison, we also ensured that the traditional template-based models we used were not informed by any structures that became available after that date. In particular, we used traditional models that were published before April 30, 2018. We selected all proteins, ligands, and structural models used in this study in a systematic manner (see Methods). We focused on one class of drug targets—G-protein-coupled receptors (GPCRs)—for three reasons. First, GPCRs represent the largest class of drug targets by a substantial margin, with 35% of FDA-approved drugs targeting a GPCR (Sriram and Insel, 2018). Second, GCPR-binding pockets are very diverse, reflecting the fact that GPCRs have evolved to bind very different ligands with high specificity (Venkatakrishnan et al. , 2013). Third, the GPCRdb project maintains a historical database of template-based models of GPCRs (Pándy-Szekeres et al. , 2018). By selecting the template-based models listed in GPCRdb in April 2018, we ensured that these models were not informed by any structures that became available after April 30, 2018. Binding pocket structures are much more accurate in AF2 models than traditional models Because proteins are flexible, each protein can adopt multiple structures. In particular, the structure of a protein—especially the structure of the binding pocket—depends on which ligand is bound. To quantify this natural variation, we compared all pairs of experimentally determined structures of the same protein to one another, using root mean squared deviation (RMSD) as a metric (see Methods). AF2 does not allow one to specify a bound ligand when generating a protein model, and the template-based models listed in GPCRdb were also generated without specification of a bound ligand. We thus evaluated the accuracy of each model by computing its RMSD to that of all available experimental structures of the same protein. We compared the resulting distribution of RMSDs to the distribution of RMSDs between structures of the same protein with different ligands bound. As expected based on previously reported results for other proteins (Jumper et al. [SEP] Justify:

Output: The text does not directly address the claim about the ability of protein properties to predict their functions. Instead, it focuses on the methodology used in generating and evaluating models of G-protein-coupled receptors and discusses the structural variability of proteins based on bound ligands, without making any assertions about the predictive accuracy regarding their functions.


Input: Task: corroborate the claim using the given text. Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: Published print: 01 Feb 2012 AbstractOrganic sweet corn (Zea mays) production is challenging for growers because of the high nitrogen (N) requirements of sweet corn and the relatively low N content of organic soil amendments. supplied and rate of mineralization throughout the growing season are two important aspects in determining the optimal N management program. Green manure (GrM) crops, composted manures, and commercially available organic fertilizers are used to manage N in organic production systems. Using a combination of these tactics can optimize N while minimizing cost. In this study, we used combinations of composted poultry manure (CPM) and two organic fertilizers (one high N and one with a balance of nutrients) with three GrM crops [rye (Secale cereale), alfalfa (Medicago sativa), and pea (Pisum sativum)] in a loamy sand soil for a 112-day laboratory incubation study. Total plant available N (PAN) was quantified at six times throughout the 16 weeks to determine total N mineralized and rate of N release from each of the management programs. The CPM and the high N organic fertilizer mineralized completely within the first 6 weeks of the study, while only 65% of the other organic fertilizer mineralized by the end of the study. mineralized from pea grown as a GrM for 48 days was comparable to the amount of N mineralized from rye suggesting that pea as a GrM crop should not receive an N credit in field production on a sandy soil. Significant quantities of PAN were mineralized from alfalfa residue, the equivalent of 80 lb/acre, although this is not sufficient for sweet corn production. The combination of alfalfa and the high N organic fertilizer provided sufficient N for sweet corn production and the mineralization rate closely matched sweet corn need. The release of N from CPM, even in combination with GrM crops, was asynchronous with sweet corn crop need. Keywords: alfalfa; pea; ryeNitrogen management for optimal crop production is one of the most difficult and costly practices in organic agriculture. The majority of the N in organic sources is bound in organic compounds, which must be mineralized by the soil microbial community to become a PAN form (ammonium or nitrate). Moisture, temperature, and composition of the soil microbe community can affect the rate at which organic N is mineralized. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of composted manures and organic fertilizers in organic farming systems to manage nitrogen (N) for crops like sweet corn. It mentions that organic farming practices involve using natural sources of nitrogen such as green manure crops, composted manures, and commercially available organic fertilizers, which directly supports the idea that organic farming utilizes natural fertilizers instead of synthetic chemicals.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: [--deleted text--] Therefore, in contrast to an auditory monitoring task which showed decline in univariate graph-theoretic connectivity before behavioural errors (Sadaghiani et al. , 2015), we observed no change in informational connectivity on error. Note that, the number of trials is equalized across the 8 conditions in each of our analyses separately. [Original text: #2] There was strong evidence for less (almost half) connectivity on miss compared to correct trials (Bayes factor = 3e63). There was insufficient evidence to determine the effects of the Time on Task or (Bayes factor = 0. 33 and BF = 0. 35, respectively) and moderate evidence for a lack of two-way and three-way interactions between the three factors (Bayes factor -Target Frequency: BF = 0. 17; Behaviour-Time on Task: BF = 0. 18; Target Frequency-Time on Task: BF = 0. 16; their three-way interaction BF = 0. 11). Weaker connectivity between occipital and frontal areas could have led to the behavioural misses observed in this study (Figure 1) as was previously reported in an auditory monitoring task using univariate graph- theoretic connectivity analyses (Sadaghiani et al. , 2015), although, of course, these are correlational data and so we cannot make any strong causal inferences. These results cannot be explained by the number of trials as they are equalised across the eight conditions in each of the analyses separately. [Results: Is neural representation different on miss trials? ] [Corrected text: #1] On correct trials, the distance information for both Active and Monitoring conditions was above chance (Figure 5B left panels; BF >104). For miss trials, the corresponding distance information was still above chance (Figure 5B right panels; BF >103) but the direct comparison revealed that distance information dropped on miss trials compared to correct trials (Figure 5B… [Original text: #1] On correct trials, the distance information for both Active and Monitoring conditions was well above chance (77%; BF >10). In contrast, for miss trials, the corresponding distance information was only just above chance (55%; BF >3 for all distances). [SEP] Justify:

Output: The text contrasts the claim by discussing research findings related to neural connectivity and behavior in an auditory monitoring task, which implies limitations in prediction and inference-making capabilities based on observed data. The text emphasizes that the results are correlational and does not support the claim that AI can predict and prevent all health issues before they occur, instead indicating the lack of strong causal inferences.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Whiskers extend to last data points that are within 150% of the interquartile range, and outlier data points beyond those are shown individually. The plotted data is based on 150 RMSD values (comparisons) for experimentally determined structures with different ligands bound, 52 for AlphaFold 2 models, and 78 for traditional models. Binding pose prediction using AF2 models or traditional models yields similar accuracy Next, we assess the accuracy of ligand-binding poses predicted by computational docking to AF2 models and traditional template-based models. For each ligand we consider, the binding pose to a target protein is known based on an experimentally determined structure of the ligand bound to the protein, which we call the reference structure. Using industry-standard software, we docked each ligand to both AF2 and GPCRdb models of the protein. A predicted pose is considered correct if its RMSD from the experimentally determined pose is ≤2. 0 Å (Figure 2), a widely used criterion for correct pose prediction (Erickson et al. , 2004; Gohlke et al. , 2000; Paggi et al. , 2021). Figure 2 with 2 supplements see all Download asset Open asset Accuracy of ligand-binding poses predicted by computational docking to AlphaFold 2 models, traditional template-based models, or protein structures determined experimentally in complex with a ligand different from the one being docked. We plot the fraction of docked ligands whose pose is predicted correctly (see Methods). Error bars are 90% confidence intervals calculated via bootstrapping. *** for p-values <0. 001, ns for p-values >0. 05. 40 - 54 ligands were evaluated for each set of structures or models. For comparison, we also dock each ligand to structures of the same protein determined experimentally with other ligands bound (cross-docking). Note that when calculating pose prediction accuracy (Figure 2), we exclude cases where the ligand is docked back to the reference structure; such ‘self-docking’ yields much higher accuracy (Figure 2—figure supplement 1) but is of little practical utility, because in practice one wishes to predict binding poses that have not already been determined experimentally. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on the accuracy of ligand-binding pose predictions using computational docking and does not address the prediction of protein functions based on their properties. There are no mentions of properties of proteins or functions related to the claim.


Input: Task: corroborate the claim using the given text. Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: content of organic sources is often not completely mineralized within one growing season (Crew and Peoples, 2005; Hartz et al. , 2000; Hartz and Johnstone, 2006). Insufficient PAN in the soil during periods of high crop need can translate into crop deficiency and potentially yield loss. However, excessive PAN in the soil before crop need or at times of low crop N use can leave the nitrate portion susceptible to leaching and inefficient crop use. Ideally, the mineralization rate would match fluctuations in crop need throughout the growing season. Understanding total N contribution and mineralization rate of organic N inputs can allow a comparison with crop need. management is particularly challenging for high N-requiring crops such as sweet corn. Organic growers typically use a variety of strategies to meet crop N needs such as using N-fixing legumes as GrM crops, composted manure, and commercially available amendments (Gaskell and Smith, 2007). Composted manure is a cost-effective means of providing N to an organic crop because of its role as a waste product and relative abundance. However, composted manure presents several challenges for organic sweet corn production. It generally has low N concentration (<5%), so large quantities must be applied (Berry et al. , 2002), adding expense in application. of pest management guidelines suggest that manure also should be applied and incorporated at least 1 week in advance of sweet corn planting to reduce seed damage by seed corn maggot [Delia platura (Boerboom et al. , 2011)]. Sweet corn does not begin to take up significant quantities of N until the five-leaf stage (Doerge et al. , 1991), which can take up to 1 month after planting in the upper midwestern United States. that is mineralized from the manure during this time is vulnerable to loss from leaching or volatilization. Soil amendments that contain higher concentrations of N such as feather meal, bone meal, fish powder, and guano are available commercially in various formulations. Smaller total quantities of these amendments can be applied as they have higher N concentration, ranging 11% to 14% (Hartz and Johnstone, 2006), reducing transportation and application costs compared with manure. Hadas and Kautsky (1994) reported continuous release of PAN from feather meal in an 8-week incubation study. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of composted manure as a natural fertilizer in organic farming. It notes that organic growers employ various strategies to meet crop nitrogen needs, including using composted manure and highlights that this manure is a cost-effective means of providing nitrogen to organic crops. This supports the notion that organic farming relies on natural fertilizers instead of synthetic chemicals.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: The direct comparison revealed that distance information dropped considerably on miss trials compared to correct trials (Figure 5B… [Corrected text: #2] However, while the distribution of decoding accuracies for correct trials was centred around 60%, the decoding accuracies for individual miss trials were centred around 56%. We evaluated the difference in the distribution of classification accuracies between the two types of trials using Cohen’s d. Cohen’s d ranged from 0 to 2. 5 across participants and conditions. 14 out of 21 subjects showed moderate (d>0. 5) to large (d>0. 8; Cohen, 1969) differences between the distribution of correct and miss trials in either Active or Monitoring condition or both. Therefore, although the miss trials vary somewhat in levels of information, only a minority of (<24%) miss trials are as informative as the least informative correct trials. [Original text: #2] However, while the distribution of decoding accuracies for correct trials was centred around 80%, the decoding accuracies for individual miss trials were centred around chance-level. We evaluated the difference in the distribution of classification accuracies between the two types of trials using Cohen’s d. Cohen’s d was approximately 3 or higher for all participants and conditions, indicating a large (d>2; Cohen, 1969) difference between the distribution of correct and miss trials. Therefore, although the miss trials vary somewhat in levels of information, very few (<7%) miss trials are as informative as the least informative correct trials. [Corrected figure: Figure 5B] [Original figure: Figure 5] [Corrected figure: Figure 5—figure supplement 1] [Original figure: Figure 5—figure supplement 1] [Corrected figure: Figure 5—figure supplement 2B] [Original figure: Figure 5—figure supplement 2] [Results: Can we predict behavioural errors using neuroimaging? ] [Corrected text: #1] This was ~0. 4 standard deviation below the average accuracy on the other participants’ validation (correct trial) sets (Figure 6C). [Original text: #1] This was ~1. 5 standard deviation below the average accuracy on the other participants’ validation (correct trial) sets (Figure 6C). [Corrected text: #2] The prediction accuracy of behavioural outcome was above chance level (59% vs 50%; BF >10)…. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of predictive accuracy in AI systems. It states that the decoding accuracies for individual miss trials were only around 56% to 59%, which suggests that AI may not be able to predict all health issues accurately. The mention of 'very few (<7%) miss trials are as informative as the least informative correct trials' indicates that the predictive capabilities of AI are limited and cannot guarantee prevention of all health issues.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Future protein structure prediction methods might instead be designed to maximize utility for ligand-binding prediction, while still exploiting the deep-learning advances of AF2 and related recent methods. We also note that the vast majority of test cases used in the design of currently available docking software involved experimentally determined protein structures. One might be able to redesign docking methods to yield better performance given computationally predicted models. and ligand selection We selected structures from the aggregated list of all experimentally determined human GPCR structures from GPCRdb (Isberg et al. , 2014). We removed all proteins for which a structure was published in the PDB before April 30, 2018, including two for which structures of the extracellular domain (to which ligands bind) had been published before that date. We also removed proteins for which structures were available in complex with fewer than two unique orthosteric ligands. This left us with 54 experimentally determined structures of 18 GPCRs (17 class A, 1 class B). The full list of proteins and structures is in Supplementary file 1. Obtaining structural models AlphaFold 2 For each protein, we took the full protein sequence from Uniprot (UniProt Consortium, 2023) to generate five AF2 models, as was done for CASP (Jumper et al. , 2021), and picked the top-scoring prediction as our model. We set the template cutoff date to 2018-04-30. GPCdownloaded template-based models from the GPCRdb archive dated 2018-04-06. We used all the inactive- and active-state models available, and averaged results as described in Statistical tools. We excluded models for which the minimum sequence identity of the templates was below 40%, as such template-based models are typically not used for drug-binding predictions. This led to exclusion of models for four GPCRs (AGRG3, TA2R, PE2R2, and PE2R4). GPCRdb did not include models for two of the GPCRs (GPBAR and PE2R3). Ligand preparation Ligands were extracted from PDB structures using the Schrödinger Python API and then manually inspected to make sure we had chosen the ligand at the orthosteric site. With the same API, ligands were converted to strings. [SEP] Justify:

Output: The text does not directly discuss whether the properties of proteins can accurately predict their functions. Instead, it focuses on protein structure prediction methods related to ligand-binding prediction and discusses various aspects of model generation and docking methods. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: The soil was air-dried, and plant material was sieved out of the soils and retained, coarsely chopped with a knife into 0. 01 to 0. 02-inch pieces and mixed back into the soil as uniformly as possible. Composted, pelletized poultry manure was used as a composted manure source for the study. A feather meal based amendment, Fertilizer 11-0-0 (advertised content 11N–0P–0K; Co. , Rowley, MA), and Fertilizer 5-5-5 (advertised content 5N–2. 2P–4. 2K, Co. ), were used as representatives of commercially available organic fertility management products. Ammonium nitrate was used as a conventional control. The CPM and amendment sources were analyzed, on a dry matter basis, for actual macronutrient content. CPM contained 4. 4% N, 1. 8% P, and 2. 5% K; Fertilizer 11-0-0 (hereafter referred to as 11-0-0) contained 10. 1% N, 0. 5% P, and 0. 6% K; Fertilizer 5-5-5 (hereafter referred to as 5-5-5) contained 6. 1% N, 1. 8% P, and 4. 8% K; ammonium nitrate contained 34% N. Treatments were designed to add CPM, 11-0-0, and 5-5-5 to the soils at a rate of 150 lb/acre, the recommended rate of N application for sweet corn in Wisconsin (Laboski et al. , 2006). The amount of CPM applied was determined using the N content from the laboratory analysis. Organic fertilizers were applied based on advertised N rates. We made this decision because growers typically have manure analyzed because of variability in macronutrient content. On the other hand, advertised macronutrient content of commercially available amendments is regulated and generally accepted by growers as accurate without further testing. The study was a completely randomized design with three replications of each soil/amendment combination. There were four cover crop treatments (alfalfa, pea, rye, and none) and five amendment treatments (11-0-0, 5-5-5, CPM, ammonium nitrate, and none), resulting in 20 treatments. [SEP] Justify:

Output: The text corroborates the claim by detailing the use of composted poultry manure (CPM) as a natural fertilizer in the study. It explicitly states that 'composted, pelletized poultry manure was used as a composted manure source for the study,' supporting the idea that organic farming leverages natural fertilizers rather than resorting to synthetic options like ammonium nitrate.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: [Original text: #2] The prediction accuracy of behavioural outcome was above chance level (68% vs 50%; BF >10)… [Corrected text: #3] The accuracy increased to 65. 4% as the dot approached the centre of the screen, with ~64% accuracy with still 800ms to go before required response. [Original text: #3] The accuracy increased to 85% as the dot approached the centre of the screen, with ~80% accuracy with still 800ms to go before required response. [Corrected text: #4] The prediction of behavioural outcome (Figure 6) was performed using the data from the whole data set. To test if prediction accuracy depended on the stage of the experiment, we performed the behavioural prediction procedure on data sets obtained from the first 5 (early) and the last 5 (late) stages of the experiment separately (Figure 6—figure supplement 1). There was no evidence for a change in the prediction power in the late vs. early blocks of trials. [Original text: #4] The prediction of behavioural outcome (Figure 6) was performed using the data from the whole data set. However, it is possible that the prediction would not be as accurate in later stages of the experiment (compared to the earlier stages) as the decoding performance of the distance information declined in general in later stages (Figure 3B). To test this, we performed the behavioural prediction procedure on data sets obtained from the first 5 (early) and the last 5 (late) stages of the experiment (Figure 6—figure supplement 1). There was strong evidence for a decline in the prediction power in the late vs. early blocks of trials. However, even with the decline in prediction accuracy, it is still possible to predict the behavioural outcome in the late blocks with well above-chance accuracy (up to 75%). [Corrected figure: Figure 6B,C,D] [6 caption sections] Results showed highest prediction accuracies on the participant set at around the threshold of 0. 4SD under the decoding level for correct validation trials, increasing at closer distances. Results showed successful (~=59%) prediction of behavioural outcome of the trial as early as 80 ms after stimulus appearance. [6 caption sections] Results show highest prediction accuracies on the participant set at around the threshold of 1. 5SD under the decoding level for correct validation trials, increasing at closer distances. [SEP] Justify:

Output: The text contrasts the claim by detailing that while the prediction of behavioral outcomes can achieve above-chance accuracy (with specific figures like 75% in later experimental stages), it emphasizes limitations, such as a decline in prediction power in later stages of the experiment. It suggests that while AI can predict some outcomes, there are significant qualifications and a failure to ensure predictions are fully accurate or universal, especially concerning health issues.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Ligands were then prepared with Schrödinger’s LigPrep with default command line parameters (Schrödinger LLC, 2021). Ligand similarity was defined as a ratio of the size of the maximum common substructure to the size of the smaller molecule. Ligand pairs for which this ratio was less than 0. 5 were deemed very different ligands. Protein preparation Experimentally determined structures were downloaded from the PDB. Only a single chain containing the ligand was kept, and all waters were removed. All structures and models were prepared with Schrödinger Wizard (Schrödinger LLC, 2021 ) following the same protocols as Paggi et al. , 2021, including energy minimization. All the experimentally determined structures were determined with a ligand bound, and we retained this ligand during minimization. Computationally predicted models did not include a ligand. Glide docking XP and Glide SP docking, we follow the docking protocol described in Paggi et al. , 2021. The grid is centered at the geometric centroid of the ligand and is defined with an inner box with 15 Å sides and outer box with 30 Å sides. For predicted structural models the center of the box is determined by aligning the model to an experimentally determined structure of that protein and using the centroid of its ligand. For this alignment, we pick the structure with the alphabetically first PDB entry ID. Rosetta docking docking, we used the GALigandDock protocol (Park et al. , 2021). To prepare the proteins, PyMol (Schrödinger, 2015) was used to remove heteroatoms and all alternative locations for each atom. information was included. UCSF Chimera v1. 16 (Pettersen et al. , 2004) was used to initially remove all hydrogens, followed by their software to prepare the structure for docking. For ligands, v2. 4. 0 (O’Boyle et al. , 2011) was used to generate 3D structures from ligand and add hydrogens at a pH of 7 (O’Boyle et al. , 2011). Ambertools v22. 0 (Case et al. , 2023) was used to add AM1-BCC partial charges. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily focuses on the procedures and protocols for preparing ligands and proteins for computational modeling and docking. There is no direct discussion or evidence regarding the argument that the properties of proteins can accurately predict their functions.


Input: Task: corroborate the claim using the given text. Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: , 2017; Olufolaji et al. , 1990). Crop residues return small amounts of organic materials to the soil as a way of nutrient recycling. Agricultural waste and food/industrial by-products are organic materials that could be good sources of organic soil amendments needed to maintain soil fertility at levels that can support the growth of amaranth. Manures serve as organic fertilizers in many agronomic production systems. They add soil organic matter (SOM) and decompose it into stable humic substances (Davison et al. , 2000), while supplying macro- and micronutrients (Usman, 2015). As a reservoir of plant nutrients, SOM needs to be manipulated to promote soil fertility and ensure success in humid tropical agriculture (Obalum et al. , 2011). Accretion of SOM promotes soil aggregation and structure of coarse-textured soils to enhance water retention, thereby slowing down nutrient mineralization, and favouring sustenance of beneficial soil organisms (Obalum et al. , 2017). Conventional animal manures are often used as organic soil amendments in amaranth production (Ainika, 2010; Bawa, 2010). However, poultry droppings (PD) can be scarce, and this is aggravated by its other important uses, including serving as a substrate in the bioremediation of crude-oil polluted soils (Ezenne et al. , 2014). [SEP] Justify:

Output: The text corroborates the claim by stating that 'manures serve as organic fertilizers in many agronomic production systems,' indicating that organic farming employs natural fertilizers like manure rather than synthetic chemicals. Additionally, it discusses the role of organic materials and manures in maintaining soil fertility and their benefits in agriculture, which supports the claim regarding the reliance on natural fertilizers.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: Results showed successful (~=70%) prediction of behavioural outcome of the trial as early as 80 ms after stimulus appearance. [Original figure: Figure 6] [Corrected figure: Figure 6—figure supplement 1] [Original figure: Figure 6—figure supplement 1] [Discussion] [Corrected text: #1] Using the sensitive analysis method of MVPA, we showed that neural coding was stronger for attended compared to distractor information. There was also one time-window where the interaction between the time on the task and target frequency affected decoding, with a larger decline in coding under monitoring conditions, which may reflect a neural correlate of the behavioural vigilance decrements. [Original text: #1] Using the sensitive analysis method of MVPA, we showed that neural coding decreased for relevant task information more when targets were infrequent than frequent (at longer task durations), providing neural correlates of the behavioural vigilance decrements. [Corrected text: #2] We also developed a novel informational brain connectivity analysis, which showed that the correlation between information coding across peri-occipital and peri-frontal areas varied with different levels of attention but did not change with errors. [Original text: #2] We also developed a novel informational brain connectivity analysis, which showed that the correlation between information coding across peri-occipital and peri-frontal areas varied with different levels of attention, target frequency and the time on the task. [Corrected text: #3] Finally, we utilised our recent error data analysis to predict forthcoming behavioural misses based on the neural data. [Original text: #3] Finally, we utilised our recent error data analysis to predict forthcoming behavioural misses with high accuracy based on the neural data. [Corrected text: #4] When targets were infrequent, modelling real-life monitoring situations, there was a strong behavioural drop in performance (i. e. , vigilance effects in both accuracy and RT; Figure 2) and a hint in the brain activity data of a change in neural coding (namely one time-window showing evidence of an interaction between and Time on Task). We need more data to fully test this effect, however, our main finding is that of being able to use the difference in decoding between correct and miss trials to predict behaviour. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of predicting behavioral outcomes from neural data. It specifies that while there was a successful prediction of behavioral misses based on neural data, the results are not comprehensive for all health issues and state, 'We need more data to fully test this effect,' indicating that predictions are not absolute and highlight that AI's capabilities are restricted, thus undermining the claim.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: The ligand was translated such that the center of mass of the ligand was positioned at center of the binding pocket of the prepared structure. The center of the binding pocket was defined as the center of mass of the ligand in the experimentally determined structure. Models were aligned to the experimentally determined structure of the protein which had the alphabetically first PDB entry ID. This structure’s ligand center of mass was used to define the model’s binding pocket. Structural comparisons To calculate ligand pose RMSD, structures were first aligned on amino acid residues within 15 Å of bound orthosteric ligands, using Schrödinger’s structalign tool (Schrödinger LLC, 2021). This alignment was used to calculate the RMSD of each docked ligand pose from the reference ligand pose. Binding pocket RMSDs were calculated with a PyMOL script, considering all residues that are within 5 Å of the ligand in the reference structure (for both alignment and RMSD calculation). We included all non-hydrogen atoms in this calculation, except that for the backbone-only binding pocket RMSDs shown in Figure 5—figure supplement 1 we included only backbone atoms. We computed full-structure RMSDs with a similar PyMOL script, but taking into account all non-hydrogen atoms in the protein. 3, Figure 4, and Figure 3—figure supplement 1, we highlight residues in the binding pocket whose positions differ most from the reference structure. To identify these residues, we aligned to the reference structure the structure and model (or the two models) being docked to. For each structure/model, we computed the RMSD for each residue within 5 Å of the ligand, and then for each residue determined the maximum RMSD across the two structures/models. We selected the residues with the largest maximum RMSDs. Statistical tools p values were computed with two sided paired t-tests, using SciPy (Virtanen et al. , 2020). Bootstrapping 90% confidence intervals were computed with the default 9999 resamples, also using SciPy. 2, the docking accuracy is calculated as a weighted average across all docking results, with weights chosen such that each ligand–protein pair is represented equally. [SEP] Justify:

Output: The text does not address the claim regarding the predictive abilities of protein properties concerning their functions. Instead, it focuses on technical aspects of ligand pose RMSD calculations and structural comparisons in protein-ligand interactions, which are unrelated to the stated claim about protein function prediction.


Input: Task: corroborate the claim using the given text. Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: Cattle dung (CD) that is not scarce has a high carbon-nitrogen (CN) ratio and mineralises less readily in the soil (Chukwuma et al. , 2024; Ndzeshala et al. , 2023; Uzoh et al. , 2015). Composting of organic materials before soil application ensures the stability of SOM and the release of nutrients to the soil (Baiyeri et al. , 2020; Tiquia, 2003). Amaranth growth can be increased with soil application of composted plant residue and a mix of animal waste (Dada et al. , 2017). One potential constraint, however, is the short-lived nature of the compost effect on soil structure stability (Obalum et al. , 2019). These prospects and constraints of composts and of PD and CD manures (in terms of availability and mineralization rates) offer an opportunity to explore the prospects of mixing plant waste with animal manures before composting, thereby developing sustainable enhanced productivity of amaranth and similar leafy vegetables all-year-round. Any lack of residual effects of conventional compost on soil structure stability (Obalum et al. , 2019) and yield of vegetable crops may be compensated for by PD and CD manures (Ogunezi et al. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of compost and animal manures (specifically cattle dung and plant residues) in organic farming practices. It highlights the benefits of composting organic materials and the applications of composted plant residues and animal waste to enhance soil health and increase crop yield, which aligns with the principles of organic farming that favor natural fertilizers over synthetic chemicals.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: [Original text: #4] When targets were infrequent, modelling real-life monitoring situations, the neural coding of crucial information about the task dropped, correlating with the decrease in behavioural performance (i. e. , vigilance effects in both accuracy and RT; Figure 2). [Corrected text: #5] When people miss targets, they might process or encode the relevant sensory information less effectively than when they correctly respond to targets. [--deleted text--] [Original text: #5] One explanation for the decrease in decoding accuracy for task-relevant information could be that when people monitor for rare targets, they process or encode the relevant sensory information less effectively as the time passes, relative to conditions in which they are actively engaged in completing the task. [Corrected text: #6] [--deleted text--] [Original text: #6] For example, the drop in decoding over time for both Active and Monitoring that is seen in Figure 3 might reflect some of the general changes in the characteristics of the recording hardware over the course of the experiment (e. g. , the MEG system warming up), but our design allows us to dissociate these from the key vigilance effects we are interested in. [Corrected text: #7] In contrast, the task-relevant information of distance to object was affected by attention and was attenuated on errors. [Original text: #7] In contrast, the task-relevant information of distance to object was affected by attention, target frequency and time on task and was dramatically attenuated on errors. [Corrected text: #8] Our information-based brain connectivity method showed moderate evidence for no change in connectivity between correct and error trials. [--deleted text--] Informational connectivity is unaffected by differences in absolute levels of information encoding (e. g. , lower coding on miss vs. correct trials). It could be sensitive to different levels of noise between conditions, but there was no evidence for that in this case. Apart from sensory information coding and sensory-based informational connectivity, which were evaluated here, there may be other correlates we have not addressed. [Original text: #8] Third, our information-based brain connectivity method showed weaker connectivity between the peri-frontal attentional network and the peri-occipital visual areas of the brain in the unattended and monitoring conditions (Figure 4), where participants encountered fewer targets relative to the other conditions. [SEP] Justify:

Output: The text contrasts the claim by discussing how the neural coding of crucial information drops with infrequent targets and how people might process sensory information less effectively, suggesting limitations in predictive capabilities. It emphasizes the complexity of human cognition and information processing which the AI models may not fully account for, undermining the notion that AI can fully predict and prevent health issues.


Input: Task: pass the claim using the given text. Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: In particular, when multiple traditional template-based models of a single protein were available, we docked each ligand to each model but set the averaging weight to the inverse of the number of models docked to. Likewise, when multiple experimentally determined structures of a single protein were available for docking of a particular ligand (i. e. , structures determined experimentally without that particular ligand bound), we docked the ligand to each structure but set the averaging weight to the inverse of the number of structures docked to. 5 and Figure 5—figure supplement 1, instead of simply binning structures and models in different RMSD ranges, we performed a smoothing analysis that does not impose arbitrary cutoffs between bins. The smoothed functions were created by running kernel smoothing over docking outcomes. The smoothing was done over data such that each data point was the binding pocket RMSD and the binary value indicating whether or not the predicted pose was correct. kernel (Hastie et al. , 2009) was used, with a width of 0. 25 Å. To avoid inaccurate boundary condition behaviors, each end the kernel was only evaluated when the center of the kernel overlapped with the first or last data point on the X-axis. To calculate statistical significance and p values, a bootstrapping significance test was conducted for each interval on the X-axis. Additionally, in Figure 5—figure supplement 1, we show 90% confidence intervals that are also computed with bootstrapping. Add a comment + Open annotations. The current annotation count on this page is being calculated. 9. 0ödingerPyMOL 3. 8SoftwareAlphaFold 2. 0. 1 Data availability Data and analysis scripts are supplied in The following data sets were generated (2023) and structures for "How accurately can we predict binding poses with AlphaFold models? . 797. PubMed 876. PubMed 242. PubMed 3277. PubMed 98. PubMed 778. PubMed 1674. PubMed 55. PubMed 3421. PubMed 1749. PubMed 6196. PubMed 356. PubMed 7449. PubMed 10. 1007/978-0-387-84858-7 7. PubMed 1885. PubMed 425. PubMed 589. PubMed 436. PubMed 1617. PubMed 308. PubMed 167. PubMed 10. 1101/2022. [SEP] Justify:

Output: The text does not address whether the properties of proteins can predict their functions accurately. Instead, it discusses methodological details and approaches involved in protein docking, including kernel smoothing and statistical significance testing. Thus, it is irrelevant to the claim.