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: , 2019; Onah et al. , 2023; Sanya & Amadji, 2018). Brewery waste (spent grain) is an industrial by product that is generated in large quantities and poorly disposed (Devolli et al. , 2018; Nassary & Nasolwa, 2019; Onwe, 2004). One way of recycling brewery spent grain (BSG) could be by composting it into organic fertilizers, as composted BSG has a high potential for releasing essential nutrients for plant growth (Ebido & Ndubuaku, 2019; Kanagachandran & Jayaratne, 2006). Owing to the prevailing abundant discharge of its source and its ability to enhance soil fertility, composted BSG may appeal to subsistence vegetable farmers, and may be deemed a promising suitable and economically viable option for them. Little is currently known of the efficacy of complementing composts with animal manures of varying CN ratios, or the suitable combining ratio, in organic amaranth farming. This study was undertaken to determine the agronomic effects of a composted mix of BSG with PD or CD on amaranth growth and yield in acid sandy tropical soils. 2. Materials and Methods2. [SEP] Justify:

Output: The text corroborates the claim by discussing how composted brewery spent grain (BSG), which is a natural byproduct, can be used in organic farming. It highlights the potential of composting BSG as a means to produce organic fertilizers that enhance soil fertility, thereby aligning with the notion 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: We also observed less connectivity between the same areas on miss vs. correct trials. Our connectivity is unaffected by the absolute lower level of information encoding in the brain on miss vs. correct trials, but it is possible that it is driven by noisier patterns of information encoding in miss (vs. correct) trials. Thus, the lower connectivity for miss vs. correct trials could result from (at least) two possible causes: the pair of regions representing two distinct sets of information (i. e. , becoming in some sense less connected on misses) or the pair represent similar information that is distorted by higher levels of noise on misses. Apart from sensory information coding and sensory-based informational connectivity, which were evaluated here and provide plausible neural correlates for the vigilance decrement, there may be other correlates we have not addressed. [Corrected text: #9] This makes our analysis sensitive to different aspects of connectivity compared to conventional univariate analyses. [Original text: #9] This makes our analysis more sensitive for capturing subtle connectivity than the conventional univariate analyses. [Corrected text: #10] We statistically compared the two types of trials and showed a reliable advantage in the level of information contained at individual-trial-level in correct vs. miss trials. Our error prediction results showed a reliable decline in the crucial task-relevant (i. e. , distance to object) … [Original text: #10] We statistically compared the two types of trials and showed a large advantage in the level of information contained at individual-trial-level in correct vs. miss trials. Our error prediction results showed a large decline in the crucial task-relevant (i. e. , distance to object) … [Corrected text: #11] Here, we obtained up to 65% prediction (with a chance level of 50%), suggesting our method accesses relevant neural signatures of attention lapses, and may be sensitive in discriminating these. [Original text: #11] Here, we obtained up to 85% prediction (with a chance level of 50%), suggesting our method accesses more relevant neural signatures of vigilance decrements, or is more sensitive in discriminating these. [Corrected text: #12] We observed that the neural representation of critically relevant information in the brain [--deleted text--] was particularly poor on trials where participants missed the target. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of the prediction method. It emphasizes that while a certain level of prediction is achieved (up to 85% prediction with a chance level of 50%), this does not imply that AI can predict and prevent all health issues. Instead, it points out that there are likely factors, such as higher noise levels and connectivity issues, that complicate accurate predictions. Furthermore, it states that the analysis is sensitive to various aspects of connectivity rather than providing a definitive prediction of 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: News FeatureReflected in Grant Allocations, Researchers ArgueNovember 8, 2022• Physics 15, 173Researchers call for reform in how funding is allocated in response to a study that finds racial disparities in the Foundation’s awarding of research grants. JHVEPhoto/stock. adobe. com A study of Foundation data shows significant differences in the funding of grants based on the race of the applicant. JHVEPhoto/stock. adobe. com A study of Foundation data shows significant differences in the funding of grants based on the race of the applicant. × Over the years, physicist Andrea Liu, a second-generation Asian American, has been subjected to a variety of disparaging remarks. One colleague, for example, told the University of Pennsylvania professor, “it’s obvious that English is not your native language, but I can understand you just fine,” a statement she describes as “clueless. ” On another occasion, a reviewer on a Foundation (NSF) grant proposal wrote, “Liu should collaborate with her former postdoc to make sure the work is feasible,” feedback that Liu calls “pretty condescending. ” Liu attributes some of these remarks to sexist ideology and outdated ideas of gender roles. But it is the systemic racial prejudice that she finds the hardest to shake. A soon-to-be-published analysis of grant money allocations suggests that Liu is likely not alone in that feeling [1]. The study—which has been accepted in the peer-reviewed journal eLife—finds pervasive disparities in the funding rates for scientists from different racial groups, with white scientists at the top and Asian scientists at the bottom. The team behind the study says that the way funding has been allocated reflects systemic racism and calls on the NSF, a US federal agency, to eliminate the disparities. The conclusions echo outcomes of a 2011 study that looked at the allocations of funds by the of Health (NIH), another federal agency, and of a 2022 report from Wellcome, a charitable foundation in the UK that funds research. Many scientists have come forth to applaud the study, and some have related their own experiences of bias within the NSF. They note, however, that this study is not the final word on disparities in NSF funding, as the publicly available data do not include important administrative information about the applications as well as the racial makeup of reviewers and other persons involved in grant reviews. [SEP] Justify:

Output: The text is irrelevant (pass) to the claim, as it discusses the allocation of research grants and systemic racial disparities in funding rather than the predictive properties of proteins related to 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: 1 Characteristics of the Experimental SiteThe experiment was conducted at the Garden, Teaching & Research Farm, University of Ibadan, Ibadan, Nigeria (7° 24’N, 3° 54’E; 62 m asl). The farm is located in the derived savannah of southwestern Nigeria. The mean monthly temperature ranges from 24 to 30℃ and the mean annual rainfall from 1,800 to 2,100 mm. The field study was conducted on an acid sandy soil. Some physicochemical properties and fertility indices of the top 0-20 cm soil before the study were assayed following standard laboratory procedures (Sparks, 1996). 2. 2 Treatments, Field experiments were carried out to determine effects of BSG-based compost on the growth and the yield of green amaranth as organic soil amendments. The BSG was composted with and without PD and CD, involving soil application of composted BSG and a composted mix of BSG with PD or CD. The PD and CD were chosen as manures because of their known wide difference in CN ratio, with smaller values in the former than the latter (Adubasim et al. , 2018), which translate into their faster and slower mineralisation, respectively in coarse-textured tropical soils (Chukwuma et al. , 2024). The BSG was supplied by the the PD and CD were obtained from the Teaching & of the Department of Animal Science, Faculty of Agriculture and Fisheries, University of Ibadan, Ibadan, Nigeria. The composting of BSG and its mix with PD or CD was done under controlled well-aerated environmental conditions in the Garden of the Teaching & Research Farm. In initiating the composting, these three organic wastes were used in their fresh forms as procured and were not subjected to curing by sun-drying. Each of BSG+PD and BSG+CD was composted in volume ratios of 1:1 and 2:1, each having two replications. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of organic materials such as BSG (brewery spent grain), PD (poultry dung), and CD (cow dung) as compost for organic amendments in farming. It specifically mentions 'composted BSG and a composted mix of BSG with PD or CD,' which directly supports the notion that organic farming utilizes natural fertilizers rather than 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: #12] We observed that the neural representation of critically relevant information in the brain decreases over time, especially when targets are infrequent. This neural representation was particularly poor on trials where participants missed the target. [Corrected text: #13] We used this observation to predict behavioural outcome of individual trials and showed that we could predict behavioural outcome… [Original text: #13] We used this observation to predict behavioural outcome of individual trials and showed that we could accurately predict behavioural outcome… [Corrected text: #14] These results provide new insights about how momentary lapses in attention impact information coding in the brain and propose an avenue for predicting behavioural errors using novel neuroimaging analysis techniques. [Original text: #14] These results provide new insights about how vigilance decrements impact information coding in the brain and propose an avenue for predicting behavioural errors using novel neuroimaging analysis techniques. [Materials and Methods] [Added text: #1] Each distance covered a time window of ~80ms (varied slightly as dot trajectories varied in angle) which consisted of 4 or 5 signal samples depending on which of the 15 predetermined distances was temporally closest to each signal sample and therefore could incorporate it. [Added text: #2] As within-trial autocorrelation in signals could inflate classification accuracy (signal samples closer in time are more similar than those farther apart), we ensured that in every cross-validation run and each distance, the training and testing sets used samples from distinct sets of trials. To achieve this, trials were first allocated randomly into 10 folds, without separating their constituent signal samples. This way, the 4 or 5 signal samples from within each distance of a given trial remained together across all cross-validation runs and were never split across training and testing sets. [Corrected text: #1] The optimal threshold was 0. 4 (±0. 07) times the SD below the decoding accuracy on the validation set across participants. [Original text: #1] The optimal threshold was 1. 54 (±0. 2) times the SD below the decoding accuracy on the validation set across participants. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of predicting outcomes based on neural representation and attention lapses. It mentions that the ability to predict behavioural outcomes is contingent upon observations regarding attention rather than suggesting that AI or predictions are comprehensive for all health issues. This implies that while there may be predictive capabilities, they are not all-encompassing, which directly counters 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: Everyone agrees that scientists need full access to all these details to uncover the true extent—and the causes—of any biases. new study—which took two years to complete—was led by Chen, a postdoctoral researcher at in California. Chen, who identifies as Asian, says she was spurred into action after a Zoom call in which a senior colleague, who also identifies as Asian, commented that it is “an ‘open secret’ that Asian scientists have a harder time getting funding from the NSF” than other racial groups. Shocked at the comment, Chen wanted to learn more, as did others on the call, including Chen’s friend Sara Kahanamoku, a graduate student in historical marine ecology at the University of California, Berkeley, who identifies as and uses the pronoun they. from the University of Pennsylvania has been subjected to a variety of racially disparaging remarks over the course of her career. from the University of Pennsylvania has been subjected to a variety of racially disparaging remarks over the course of her career. × The duo delved into the publicly available NSF data on the race and ethnicity of the principal investigator (PI) on each grant application. They found lower-than-average funding rates for applications from Asian scientists as well as from other minority racial groups. Chen and Kahanamoku then contacted researchers from across the sciences to help them carry out a full analysis of the data. They wanted the team to include social science and policy experts and to reflect the full diversity of racial groups funded by the NSF. They also wanted the help of more senior researchers, as they worried that otherwise “nobody would listen to us,” Chen says. The study lays out some stark findings. For over two decades, white scientists have consistently surpassed their peers in gaining NSF grants. In 1999 their funding success rate was 2. 8% above the overall average. By 2019 that rate had increased to 14. 3% above the average. In contrast, the success rate for Asian scientists has been persistently low, regularly dropping under 20% of the average. The rates for Black and Latino scientists hovered somewhere in between those for whites and Asians. The team found the same trend across science disciplines. “We were shocked by the consistency of the disparities that have held for over two decades,” Kahanamoku says. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses issues related to racial disparities in funding for scientists, specifically focusing on Asian, Black, and Latino scientists' experiences with NSF grants. The claim about proteins predicting functions is not addressed in this text.


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 composted organics were designated BSG+PD(1:1), BSG+PD(2:1), BSG+CD(1:1), and BSG+CD(2:1). The composting period was six weeks with temperature readings taken daily using a compost thermometer. To ensure aeration within the compost pile, the mass was turned at the beginning of the second, fourth and fifth weeks when the pH of the compost was determined and the pile moistened. The temperature of the compost reflecting the activity of micro-organisms and the pH of the compost together were used to infer its maturity. After the composting period, composts were spread and left for curing (air-drying to reduce pathogenic load) for four weeks before use. The field experiment, described by Ebido & Ndubuaku (2019), involved composted BSG+PD(1:1), BSG+PD(2:1), BSG+CD(1:1), BSG+CD(2:1), BSG as a reference, and an unamended control as a treatment. The experimental arrangement was a randomized complete block design with four replicates. Plots measuring 1. 5 × 1. 0 m were guarded against external interferences with earthen bunds (compactly prepared ridges) and were 0. 50 m apart. Manual hoeing to about 20-cm depth was done in all plots. Thereafter, composted organic amendments were uniformly spread on the surface of the plots at the rate of 20 t ha–1. A pack of amaranth seeds, of cv. PI 511679, was purchased from Eagle Seeds, Orogun Ibadan, Nigeria. This genotype, grown mainly for its leaves, usually grows up to 25 cm tall with about 11 leaves in about four weeks on un-amended soils in the study environment (Adeoluwa et al. , 2009). Three days after hoeing the soil and spreading the composted organic amendments, seeds were sown in drills spaced 0. 50 m apart. [SEP] Justify:

Output: The text corroborates the claim by detailing a process that specifically involves composting, which is a natural method of creating fertilizer. The description of composted organic amendments being used in the field experiment reinforces the idea that organic farming principles typically favor natural over synthetic fertilizers.


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 10. 7554/eLife. 60563 Open access Copyright information Version of RecordAugust 8, 2023 Version of RecordApril 21, 2021 Accepted ManuscriptApril 8, 2021 Open annotations (there are currently 0 annotations on this page). 1,414 views 170 downloads 15 0563. Copy to clipboard Download BibTeX Download. RIS Full text Figures and data Peer review Side by side Peer review process Decision letter Author response Peer review process This 1. The introduction makes it clear that the authors acknowledge that there may be multiple sources of interference contributing to declining vigilance over time: the encoding of sensory information, appropriate responses to the stimuli, or a combination of both. In the introduction, it would help if the authors review how infrequent targets affect response patterns. In addition, it would help if the theoretical approach and assumptions of the authors were explicitly stated. For instance, the a priori assumptions surrounding the connectivity analysis should be acknowledged and discussed in the interpretation of the pattern of results (e. g. , p. 32, line 658). Specifically, the focus on connectivity between frontal and occipital areas seems to assume the effects are related to sensory processing alone, but this does not preclude other influences. For instance, effects could also occur on response patterns. These considerations should be added as caveats to the interpretation. 2. It is not clear what role eye fixations play here. Participants could freely scan the display, so the retinotopic representations would change depending on where the participants fixate, but at the same time the authors claim that eye position did not matter. Materials and methods, Page 11: The authors state that "We did not perform eye-blink artefact removal because it has been shown that blink artefacts are successfully ignored by multivariate classifiers as long as they are not systematically different between decoded conditions (Grootswagers et al. , 2017). " This is not a sufficiently convincing argument. Firstly, the cited paper makes a theoretical argument rather than showing this empirically. Secondly, even if this were true, the frequency of eye-related artefacts seems to be of crucial importance for a paradigm that involves moving stimuli (and no fixation). There could indeed be systematic differences between conditions that are then picked up by the classifier (i. e. if more eye-blinks are related to tiredness and in turn decreased vigilance). [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities and uncertainties in interpreting AI results related to health, particularly emphasizing that there are multiple sources of interference that can affect the outcomes. It indicates that factors such as sensory processing and response patterns must be considered, which suggests that AI does not operate in isolation to predict and prevent health issues. This acknowledgment of potential interferences implies limitations in AI's ability to 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: The team infers that white scientists have received a “surplus” of 12,820 awards over the past 20 years. Meanwhile, Asian scientists have a “deficit” of 9701 awards. and Hispanic scientists, the deficits are 417 and 175 awards, respectively. ( also have a deficit, and a surplus, but the grant submission numbers from those groups are too low to make the award numbers statistically meaningful. ) M. of the University of Wisconsin-Madison says that she is gratified that Chen, Kahanamoku, and their colleagues took the time to analyze this NSF data, which pushes back on common misconceptions about the role of race in funding decisions. M. of the University of Wisconsin-Madison says that she is gratified that Chen, Kahanamoku, and their colleagues took the time to analyze this NSF data, which pushes back on common misconceptions about the role of race in funding decisions. × Each award in the deficit column represents a person whose project went unfunded and whose career could thus have stalled, Kahanamoku says. Collectively, these experiences could lead to a significant accumulation of disadvantage for a given racial group. “That really struck me,” they say. The results confront a common misperception that NSF funding favors historically excluded racial groups, says mathematician of the University of Wisconsin-Madison, who identifies as Hawaiian and Black. “I know this [narrative] to be false, both from my own experience and from those of my peers and mentees. But it is really gratifying to have it so clearly illustrated in NSF’s own data. ” deeper into the awards process, the team analyzed the so-called panel scores for the different racial groups. All grants are analyzed in detail by reviewers who separately score the application on a scale from 1 (poor) to 5 (excellent) against a set of criteria. Based on this score, a decision is made for or against funding the project. Chen and her colleagues found that proposals for Black and Asian scientists were systematically rated lower than those of white scientists. “That result really resonated with me,” says Cesunica Ivey, a Black assistant professor of environmental engineering at UC Berkeley, who was not involved in the study. [SEP] Justify:

Output: The text discusses racial disparities in awards and funding in scientific research, focusing on the experiences of various racial groups. It does not provide any information or analysis relevant to the claim regarding the predictive properties of proteins and their functions. As such, the text 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 had a slightly acidic pH and a high level of exchangeable acidity. The available phosphorus (P) level was slightly high; the exchangeable potassium (K) and sodium (Na) indicated very low to low values according to the rating of Landon (1991). Table 1. Selected chemical properties of the soil before treatment applicationSoil PropertiesValueTexture classLoamy sandSoil pH-H2O5. 3Soil organic carbon (g kg-1)19. 20(g kg-1)2. 31(mg kg-1)23. 00Exchangeable bases and acidity (cmol kg-1)K+0. 10Ca2+1. 80Mg2+0. 40Na+0. 10H+0. 40Al3+0. 503. 2 on Parameters of AmaranthThe immediate effects of the composted BSG-based organic soil amendments showed that the treatment affected amaranth growth (Table 2). Treatment generally increased plant height, stem diameter and the number of leaves of amaranth plants compared with the un-amended control. Composted BSG+PD(1:1) produced taller plants and more leaves compared with the control; composted BSG+CD(2:1) gave the widest stem of the amaranth plants and the control the narrowest. For this stem diameter, composted BSG+PD(1:1) and BSG+PD(2:1) showed similar values. In addition, composted BSG+CD(1:1) and BSG+CD(2:1) had similar stem diameters, with the former being similar to the rest of the treatments excluding the control. Composted BSG+PD(1:1) and BSG+CD(1:1) were similar in all three growth indices; so too were composted BSG+PD(2:1), composted BSG (the reference) and the control, and composted BSG+CD(1:1), composted BSG+CD(2:1) and composted BSG. Table 2. [SEP] Justify:

Output: The text corroborates the claim by discussing the effects of composted organic soil amendments on amaranth growth, indicating the use of compost which aligns with the idea that organic farming relies on natural fertilizers. The mention of 'composted BSG-based organic soil amendments' suggests a reliance on natural materials rather than synthetic chemicals, thereby supporting the claim.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: The authors should show that their results replicate if standard artefact removal is performed on the data. Relatedly, on page 16 the authors claim that "If the prediction from the MEG decoding was stronger than that of the eye tracking, it would mean that there was information in the neural signal over and above any artefact associated with eye movement. " This statement is problematic: Firstly, such a result might only mean that prediction from MEG decoding is stronger than decoding from eye-movements, but not relate to "artefacts" in general, to which blinks would also count. Secondly, given that the signal underlying both analyses is entirely different (and the number of features), it is not valid to directly compare the results between these analyses. More detailed analyses of fixations and fixation duration on targets and distractors might indeed be strongly related to behaviour. What is decodable at a given time might just be driven by what participants are looking at. 3. One key finding was that while classifying the direction of the dots was modulated by attention, it was insensitive to many features that were captured by a classifier trained to decode the distance from the deflection. This is surprising since both are spatial features that seem hard to separate. In addition, the procedures to decode direction vs distance were very different. Do these differences still hold if the procedure used to train the two classifiers is more analogous or matched? 4. The distance classifier was trained using only correct trials. Then in the testing stage, it was generalized to either correct or miss trials. While there is a rationale for using correct trials only, could the decoding of error prediction be an artifact of the training sample, reflecting the fact that misses were not included in the training set? 5. By accumulating classifiers across time, it looks like classifier prediction improves closer to deflection. However, this could also be due to the fact that the total amount of information provided to the classifier increased. Is there a way to control for the total amount of information at different timepoints (e. g. , by using a trailing window lag rather than accumulation), or contrast the classifier that derives from accumulating information with the classifier trained moment-by-moment? 6. Predicting miss trials: The implicit assumption here is that there is "less representation" for miss trials compared to correct trials (e. g. , of distance to object). [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of AI in health predictions. It highlights issues such as the dependence on the data quality, the influence of attentional factors on prediction, and the challenges of directly comparing analyses that utilize different signals. Furthermore, it questions the robustness of predictions made by classifiers, particularly in the context of error prediction and trial types, indicating that AI's current capabilities do not encompass the ability to predict and prevent all health issues effectively.


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: She says that the dearth of Black tenured scientists means that young Black scientists can lack close relationships with mentors who might help them write research grant proposals. Starting in high school, white and Black students can have “diametrically opposed learning experiences,” she says. “That may be showing up in the panel scores. ” C. (right) with coauthor of University in front of the White House. After their preprint appeared online, the authors were invited to Washington, DC, for the August 9th signing of the CHIPS and Science Act. C. (right) with coauthor of University in front of the White House. After their preprint appeared online, the authors were invited to Washington, DC, for the August 9th signing of the CHIPS and Science Act. × Ivey also notes another problem: she has been a member of multiple grant panels and recalls cases where the discussion chair—typically a grant program manager—has flipped the consensus of the room from funding a particular grant to rejecting it. Claudia Rankins, a former NSF program officer who worked primarily with researchers from Colleges and Universities (HBCUs), had similar experiences. “What was revealed in the [new study] has been known to people at the NSF for years,” she says. Another former NSF employee, Roger Wakimoto, who was the assistant director of the NSF’s geoscience directorate, says he was “dismayed” when he first learned of the study’s results. “NSF holds the community to a very high standard when it comes to diversity,” he says. “Especially for the big awards, if you don’t have a very profound section about how you are addressing diversity, equity, and inclusion, you aren’t going to get funded. ” Wakimoto applauds the NSF for this effort but goes on to say that even while working at the NSF he questioned whether the foundation was holding itself to those same standards. “I never came away with the feeling that the answer was yes. ” Rankins now works on the other side of the grant application process, helping researchers at HBCUs write and submit NSF funding applications. She says that she rarely sees comments on a failed application indicating problems with the idea or with the methodology. [SEP] Justify:

Output: The text does not address the claim regarding researchers arguing that the properties of proteins can predict their functions. Instead, it focuses on the challenges faced by Black scientists regarding mentorship and funding in research, which is unrelated to the properties of proteins or their predictive capabilities.


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 materials in agriculture, specifically mentioning 'composted BSG+PD(1:1)' as a soil amendment and indicating its positive effects on nutrient release and plant growth. By highlighting the benefits of compost in enhancing soil properties and plant biomass yields, the text supports the assertion that organic farming employs natural fertilizers instead of synthetic ones.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: But even for miss trials, the representation is significantly above chance. However, maybe the lower accuracy for the miss trials resulted from on average more trials in which the target was not represented at all rather than a weaker representation across all trials. This would call into questions the interpretation of a decline in coding. In other words, on a single trial, a representation might only be present (but could result in a miss for other reasons) or not present (which would be the case for many miss trials), and the lower averages for misses would then be the result of more trials in which the information was completely absent. It could be that the results of the subsequent analysis (predicting misses and correct responses before they occur) are in conflict with this more pessimistic interpretation. If we understand this correctly, here the classifier predicts Distance to Object for each individual trial, and Figure 6B shows that while there is a clear difference between the correct and miss trials, the latter can still be predicted above chance level but never exceed the threshold? If this is true for all single trials, this would indeed speak for a weak but "unused" representation on miss trials. But for this the authors need to show how many of the miss trials per participant had a chance-level accuracy (i. e. might be truly unrepresented), and how many were above chance but did not exceed the threshold (i. e. might have been "less represented"). 7. The relationship between the vigilance decrement and error prediction. Is vigilance decrement driving the error prediction? That is, if errors increase later on, and the signal goes down, then maybe the classifier is worse. Alternatively, maybe the classifier predictions do not necessarily monotonically decrease throughout the experiment. Is the classifier equally successful at predicting errors early and late? 8. When decoding distance, active decoding declines from early to late, even though performance does not decline (or even slightly improves from early to late). This discrepancy seems hard to explain. Is this decline in classification driven by differences in the total signal from early to late? 9. Classifier performance was extremely high almost immediately after trial onset. Does the classifier perform at chance before the trial onset, or does this reflect sustained but not stimulus-specific information? 10. The connectivity analysis appears to be just a correlation of decoding results between two regions of interest. This means, if one "region" allows for decoding the distance to the object, the other one does too. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities and potential limitations of predicting trial miss responses and classifications in a study. It indicates that while predictions can be made above chance level, this does not guarantee accuracy in predicting every instance, especially as the analysis questions the efficacy of the classifiers and how they interact with phenomena such as vigilance decrement. This highlights the challenges and uncertainties in prediction with AI, suggesting it cannot predict or prevent all health issues effectively.


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: Rather, the panel’s comments usually imply that the researcher’s institution “doesn’t have the capacity” to support the work or that the researcher was “too ambitious” in their plan. “As a Black researcher, when you get feedback that says you were too ambitious, you know exactly why they’re not funding you,” Rankins says. On social media and in email discussion groups, scientists have largely voiced positive reactions to the airing of these concerns. “You have brought daylight to a place where there absolutely needs to be more,” (@HydroLejo), a hydrologist at University, Idaho, wrote on Twitter after reading a preprint version of the study. Experts in social inequities in the sciences have also weighed in on the study’s results. “investigator gap is very concerning,” says Donna Ginther, a white economist at the University of Kansas and the lead author on the 2011 NIH study and a 2019 follow-up. She applauds Chen and her colleagues for addressing this issue. was one of the numerous scientists to voice positive reactions to the preprint version of the Chen et al. study. was one of the numerous scientists to voice positive reactions to the preprint version of the Chen et al. study. × There have been two public voices that question the conclusions of Chen and her colleagues: the husband-and-wife physicists Charles and who work at on problems in condensed matter. Cynthia identifies as white and Charles as Hispanic. Six weeks after the preprint version of Chen et al. ’s study appeared online, the Reichhardts submitted a comment on the same preprint server [2]. The comment does not dispute the funding disparities found by Chen and her colleagues but claims that they could be explained by other factors: the increase in Asian faculty over the past 20 years and the different funding statistics for each of the NSF directorates—topical areas within the agency. Separately, Charles published a blog post on a site that hosts voices critical of diversity initiatives. The post lays out similar arguments as the comment but also includes remarks that question the professionalism of Chen and her colleagues. Chen and her colleagues declined to speak about the Reichhardts’ analysis and remarks until the comment has completed peer review. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses issues related to funding disparities and critiques of a specific study concerning social inequities in science. It does not mention or provide any information related to proteins or their functions, thus it does not support or contradict 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: 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 detailing the use of organic fertilizers such as composted market waste and PD manure in agricultural practices, specifically noting their effectiveness in achieving significant crop yields. It highlights the reliance on natural fertilizers, aligning with the assertion that organic farming often prioritizes these 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: However, this alone does not equal connectivity. It could simply mean that patterns across the entire brain allow for decoding the same information. For example, it would not be surprising to find that both ROIs correlate more strongly for correct trials (i. e. the brain has obviously represented the relevant information) than for errors (i. e. the brain has failed to represent the information), without this necessarily being related to connectivity at all. The more parsimonious interpretation here is that information might have been represented across all channels at this time. The authors show no evidence that only these two (arbitrarily selected) "regions" encode the information while other do not. To show evidence for meaningful connectivity, (a) the spread of information should be limited to small sub-regions, and (b) the decoding results in one "region" should predict the results in another region in time (as for DCM). 11. The display of the results is very dense, and it not always clear whether decoding for a specific variable was above chance or not. The authors often focused on relative differences, making it difficult to fully understand the meaning of the full pattern of results. The Bayes-factor plots in the decoding results figures are so cramped that it is very difficult to actually see the individual dots and to unpack all of this (e. g. , Figure 3). Could this complexity be somehow reduced, maybe by dividing the panels into separate figures? The two top panels in Figure 3B should also include the chance level as in A. It looks like the accuracy is very low for unattended trials, which is only true in comparison to attended trials, but (as also shown in 1) it was clearly also encoded in unattended trials, which is very important for interpreting the results. 12. While this is methodologically interesting work, there is no convincing case made for what exactly the contribution of this study is for theories of vigilance. It seems that the findings can be reduced to that a lack of decodability of relevant target features from brain activity predicts that participants will miss the target. This alone, however, does not seem to be very novel. Even if the issues above are addressed, the study only demonstrates that with less attention to the target, there is less evidence of representations of the relevant features of targets in the brain. The authors also find the expected decrements for rare targets and when participants do not actively monitor the targets. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of current brain decoding methods and the lack of convincing evidence for predictive capabilities. It indicates that while there are observations of brain activity and representation, the results are not adequately supporting a theory that AI can predict health issues. The mention of low accuracy in unattended trials and methodological challenges demonstrates uncertainty in prediction capabilities, suggesting that the claim of AI's predictive power in health issues is overstated.


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: However, they noted in conversations before the Reichhardts’ analysis came out, that they had considered both factors the Reichhardts’ raise. They also requested from the NSF access to additional administrative data on all the applications as well as the evaluations of those applications. That request went unheeded. “I find it strange that as a government agency they can withhold that information,” Wakimoto says. Chen and her colleagues along with Ginther and others I spoke to all said that this additional information on applicants—as well as information on the racial identification of the reviewers, program officers, and other relevant NSF staff—is needed to tease out the causes of the disparities. “Once [researchers] have cracked open that data, we will be able to see clearly what’s driving the differences in funding success,” Ginther says. “It behooves the NSF to do the heavy lifting of a high-quality study with all the data. ” The issue of diversity in scientific fields recently rose to the forefront with the August 9th signing of the CHIPS and Science Act, a $280 billion package that invests in research and development—primarily targeting the semiconductor industry. The legislation contains several provisions aimed at diversifying the STEM workforce, including the creation of a Officer position at the NSF. Within this emerging environment, many have called for the NSF to start implementing measures to reduce the impacts of any racial biases in the grant process. These measures include double-blind review of grants—where all identifiers of the submitting scientists are removed—and bias training for all reviewers. and Ivey are skeptical that these measures would have any real impact. “The race and core values of a person are inextricably linked…and no amount of bias training is going to change someone’s core values,” Ivey says. Liu notes that truly double-blind review is impossible to achieve. Even if the name is blanked out, “it’s really easy to figure out who the researcher is” from the application’s summary and reference list, she says. Chen and Kahanamoku point out that the NIH tried both bias training and blind-review procedures after the 2011 study that highlighted similar racial trends at the agency. However, analysis of 2019 NIH data shows that the same disparities still exist and, for some groups, have worsened. [SEP] Justify:

Output: The text is irrelevant (pass) to the claim regarding the predictive properties of proteins and their functions. Instead, it focuses on a discussion about diversity in scientific research funding and the implications of bias in grant reviews. There is no mention of protein properties or their functions, making it unrelated 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: 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 corroborates the claim by stating that composted organic materials, such as BSG (brewer's spent grain) and PD (possibly a type of organic amendment), lead to improved soil characteristics, including higher values of soil organic carbon and total nitrogen. This aligns with the principles of organic farming, which utilizes natural amendments to enhance soil health and crop productivity, in contrast to 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: How do these findings contribute to "theories of vigilance", as claimed by the authors? Author response Essential revisions: 1. The introduction makes it clear that the authors acknowledge that there may be multiple sources of interference contributing to declining vigilance over time: the encoding of sensory information, appropriate responses to the stimuli, or a combination of both. In the introduction, it would help if the authors review how infrequent targets affect response patterns. We added the relevant information about response patterns to the Introduction as below: “To date, most vigilance and rare target studies have used simple displays with static stimuli. […] Overall, vigilance decrements in terms of poorer performance can be seen in both accuracy and in reaction times, depending on the task. ” In addition, it would help if the theoretical approach and assumptions of the authors were explicitly stated. For instance, the a priori assumptions surrounding the connectivity analysis should be acknowledged and discussed in the interpretation of the pattern of results (e. g. , p. 32, line 658). Specifically, the focus on connectivity between frontal and occipital areas seems to assume the effects are related to sensory processing alone, but this does not preclude other influences. For instance, effects could also occur on response patterns. These considerations should be added as caveats to the interpretation. We have now carefully reviewed the manuscript to be sure our assumptions and approach for the connectivity analyses are explicit. We have added the suggested material to the interpretation of the pattern of results, and acknowledge the potential for other influences on the connectivity results as caveats to our interpretation. We now limit our discussion of the connectivity results as relevant to evaluating sensory aspects of information encoding (in the Materials and methods section) as below: “There are a few considerations in the implementation and interpretation of our connectivity analysis. First, it reflects the similarity of the way a pair of brain areas encode “distance” information during the whole trial. This means that we could not use the component of time in the evaluation of our connectivity as we have implemented elsewhere (Karimi-Rouzbahani et al. , 2019; Karimi-Rouzbahani et al. , 2020). Second, rather than a simple correlation of magnitudes of decoding accuracy between two regions of interest, our connectivity measure reflects a correlation of the patterns of decoding accuracies across conditions (i. e. , distances here). [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of vigilance studies and the need to acknowledge multiple sources of interference in interpreting results. It delves into the complexities of sensory information processing and response patterns, suggesting that while there may be insights about vigilance, it does not imply that AI can predict and prevent all health issues. The emphasis on the limitations and assumptions in the interpretation of connectivity analysis indicates that there are factors that challenge the overconfidence in AI's predictive abilities.


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 NSF should consider the past 10 years at the NIH as a cautionary tale of well-intentioned solutions failing to create meaningful progress,” Chen says. A. Tripati—one of Chen’s coauthors from the University of California, Los Angeles—would like to see the NSF complete a full analysis of their public and private data to look for inequities in how science is funded. A. Tripati—one of Chen’s coauthors from the University of California, Los Angeles—would like to see the NSF complete a full analysis of their public and private data to look for inequities in how science is funded. × So what could change the status quo? Ivey would like to see the makeup of all review panels represent the diversity of the community whose grants they are judging: “It’s not about quotas but about representation of views and values. ” Kahanamoku agrees. Measures such as diversity training miss the root causes of the disparities, they say. “What a person considers as a scientific question worth investigating changes a lot based on their personal background and the values they hold. ” Currently, the funding structures don’t account for those differences. To illustrate the idea, Kahanamoku turns to their own research on the impact of climate change on small Hawaiian communities. The questions they explore are very specific to these communities, such as the effect of climate change on the fish production from a specific lake. Another scientist—one without a Hawaiian background, for example—might instead ask: what are the water quality indicators for this region and how does that change over time? Although both sets of questions address climate change, studies have shown that different research frameworks have different likelihoods of receiving funding. At the NIH, for example, investigators found that proposals considered to be of fundamental or general interest were more likely to be funded than those considered to be of relevance to just one community. The former rarely had Black PIs, while the latter often had Black PIs. Whatever the outcome of the peer review of the two funding-equity studies, Chen and her team hope to see reform in grant-awarding systems. They know that it will be challenging and will take time, but they say that the appetite for change is palpable. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the need for diversity in funding and research priorities in science, particularly in relation to climate change impacts, but does not mention protein properties 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: 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 highlighting the role of compost as a natural fertilizer, stating that 'composts have elevated pH and supply N (mostly in the form of nitrates)' which implies that organic farming practices are being applied. Moreover, it notes the success of using composted materials in enhancing the vegetative growth of plants, thus supporting the idea that organic practices rely 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: Materials and methods, Page 11: The authors state that "We did not perform eye-blink artefact removal because it has been shown that blink artefacts are successfully ignored by multivariate classifiers as long as they are not systematically different between decoded conditions (Grootswagers et al. , 2017). " This is not a sufficiently convincing argument. Firstly, the cited paper makes a theoretical argument rather than showing this empirically. Secondly, even if this were true, the frequency of eye-related artefacts seems to be of crucial importance for a paradigm that involves moving stimuli (and no fixation). There could indeed be systematic differences between conditions that are then picked up by the classifier (i. e. if more eye-blinks are related to tiredness and in turn decreased vigilance). The authors should show that their results replicate if standard artefact removal is performed on the data. We appreciate the point here. There are theoretical and practical arguments that eye-related artefacts should not drive our effects, but to be sure we also now present our results with standard artefact removal as well. Overall increases in eye-related artefacts (such as blinks) over time-on-task would not be an issue, as our design relies on comparisons between Active and Monitoring, and so any general effects should have negligible impact. But for these comparisons, there may indeed be differences in the number of eye blinks – and in fact, these conditions involve different levels of attentional recruitment, which has previously shown to correlate with the frequency of eye blinks (Nakano et al. , 2013). Thus, we certainly do not want to claim that eye-related artefacts do not matter at all, but, importantly, there are two practical reasons that the effects of eye blinks should not be dominantly picked up by our classification procedure. First, the decoding analysis is time-resolved and computed in small time windows (5 ms and 80 ms, for direction and distance information decoding, respectively). For eye blink patterns to be picked up by the classifier, they would need to occur at consistent time points across trials of the same condition, and not in the other condition, which seems implausible. Second, our MEG helmet does not have the very frontal sensors where eye-related artefacts most strongly affect neural activations (Mognon et al. , 2011), but we appreciate that this does not rule out their presence altogether. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of multivariate classifiers in context with eye-blink artefacts. It highlights that there are significant practical and empirical challenges in ensuring accurate predictions in AI systems, suggesting that while there may be theoretical arguments for the effectiveness of classifiers, they are not sufficient to assert that AI can universally predict all health issues. The detailed analysis discusses conditions under which classifiers may fail, such as the need for consistent time points for artefacts to be classified correctly, thereby undermining the claim that AI can 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: Early-career scientists—and particularly those from underrepresented groups—are “desperate for a giant overhaul of the scientific enterprise,” Kahanamoku says. Chen agrees. “I wish I never had to do this work,” she says. “It has taken both a physical and mental toll on me. ” But, she says, no one else had stepped up to the plate. “These data were out there for years, in plain sight. ” Asked for their response to the findings of Chen et al. ’s paper, an NSF media representative offered a prepared statement from the agency. This statement said that the NSF’s director, Sethuraman Panchanathan, has made addressing racial funding disparities “one of the top priorities for agency progress, and [he] continues to take this [matter] seriously. ” As part of this priority, the NSF has launched a new initiative for 2023—entitled (Access for Equity and Diversity)—that aims to reduce the various barriers that minority researchers can face when applying for funding. The statement ended by noting that the NSF acknowledges “there is still much [work] to do. " Correction (8 November 2022): A previous version incorrectly stated that the PI discussed by Ivey was a person of color. They were white. –Katherine Wrightis the of Physics Magazine. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses challenges faced by early-career scientists, funding disparities in research, and NSF initiatives to address inequities rather than addressing the relationship between protein properties and 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: , 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 composted materials, specifically 'composted BSG+PD(1:1)', as a natural fertilizer that can enhance crop productivity. The mention of this compost signifies reliance on natural methods rather than synthetic chemicals in agriculture, supporting the assertion about 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: To check empirically that eye-related artefacts were not driving our effects, we re-ran our analyses with standard artefact removal as requested. We see the same pattern of results as before, for both the key task-relevant feature of distance-to-object and the less relevant feature of direction of approach. We present the full comparative analysis in Figure 3—figure supplement 2. In the paper we now state that the results replicate with artefact removal and present the additional eye-movement-corrected results in the supplementary materials. “…,we also did a post-hoc analysis in which we removed these using “runica” Analysis (ICA) algorithm as implemented by. We used the plugin (Mognon et al. , 2011) of to decide which ICA components were related to eye artefacts for removal. This toolbox extracts spatiotemporal features from components to quantitatively measure if a component is related to eye movements or blinks. For all subjects except two, we identified only 1 component which were attributed to eye artefacts (i. e. , located frontally and surpassing the ’s threshold) which we removed. For the two other participants, we identified and removed two components with these characteristics. ” Figure 3—figure supplement 2B shows the decoding results for the key task-relevant feature of distance-to-object without and with eye-related artefact removal, in the left and right panels, respectively. The main effects of attention and time on the task and the key interaction between target frequency and time on the task remain after eye artefact removal, replicating our initial pattern of results. Figure 3—figure supplement 2A shows the decoding results for the direction of approach information without and with eye artefact removal. The results again replicate those of the original analysis: as before there is a main effect of Attention but no main effect of Time on Task or Target Frequency, and no interaction. We also checked to see if our trial outcome prediction (Figure 6D) could be driven by eye artefacts by repeating our prediction procedure using the eye-movement corrected MEG data. The results (Author response image 1) show that although removal of eye artefacts seems to reduce the prediction accuracy slightly overall, it only has minimal effect on the statistics, replicating our original findings. We can still predict the outcome of the trial with >80% accuracy at closer distances. [SEP] Justify:

Output: The text contrasts the claim by discussing the capabilities of AI in predicting trial outcomes with a specific accuracy (>80%) but indicates that it does not prevent health issues before they occur. It focuses on analyzing and correcting for eye artefacts in data interpretation rather than directly addressing the prevention of health issues, thus suggesting limitations in AI's predictive capabilities.


Input: Task: pass the claim using the given text. Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: Could yeast and bacteria replace fossil fuels? What if yeast and bacteria could replace fossil fuels? Goddard and his team are working on the MeMBrane Project, which aims to find ways of using microbes to make environmentally-friendly bioproducts such as biofuel We are all aware of the potentially cataclysmic effect of continuing to rely on fossil fuels. While we might recycle our plastic, use electric cars or walk to school, efforts are being made to reduce our reliance in surprising quarters. One area where efficiency and a move away from fossil fuels is being considered is that of the use of microbes. Fossil fuel-based chemical production has been a historical tendency for some time and, while it has helped advance the field of biochemistry, it has had the unfortunate effect of releasing enormous amounts of carbon into the atmosphere. As we all now well know, these ‘non-renewable’ production methods are affecting the world’s climate. Now, there is a concerted effort to move away from such methods and instead look for alternatives that are cleaner, greener and more efficient, including biofuels. Based at in the UK, Goddard leads the MeMBrane project, which focuses on the production of what he and his team call cell factories. “Microbial cells can be grown on a variety of ‘foods’ including sugars, plant waste and even wood pulp,” explains Alan. “These sources of carbon are renewable and therefore more environmentally friendly. Bio-based routes to chemical production can also lead to purer products. ” – a biological membrane that separates the interior of all cells from the outside environment – a living thing that is too small to be seen with the naked eye YEAST – a group of single-celled fungi that may reproduce by budding, i. e. when a bud detaches and forms into a new fungus – a gram-positive, anaerobic, rod-shaped genus of bacteria named for their unique metabolism – a field of science that involves redesigning organisms for useful purposes by engineering them to have new abilities – an area of chemistry focused on designing products and processes that minimise or eliminate the use and generation of hazardous substances – a renewable energy source, made from organic matter or wastes, that can play a valuable role in reducing carbon dioxide emissions ’? Alan and his team work with strains of Saccharomyces and Propionibacteria. [SEP] Justify:

Output: The text discusses the potential of using microbes like yeast and bacteria as alternatives to fossil fuels and emphasizes the benefits of bio-based routes in chemical production. However, it does not directly address or support the claim regarding the complete replacement of natural species by engineered organisms. Instead, it focuses on the environmental benefits and efficiencies of engineered microorganisms without asserting that they could fully replace natural species.


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 corroborates the claim by discussing organic amendments and their benefits for soil in agronomic production. It highlights that the chances of realizing residual effects in the soil are generally greater with organic amendments (such as compost) compared to mineral fertilizers, supporting the idea that organic farming relies 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: Author response image 1 Download asset Open asset The accuracy of predicting behavioral outcome of trials without and with eye artefact removal. The results are for the left-out participant (averaged over all participants) using the threshold obtained from all the other participants as function of distance/time from the deflection point. Figure 6D shows the result without eye artefact removal and Author response image 1 with eye artefact removal. Thick lines and shading refer to average and one standard deviation around the mean across participants, respectively. are shown in the bottom section of each graph: Filled circles show moderate/strong evidence for either hypothesis and empty circles indicate insufficient evidence. In the Materials and methods section, we removed the sentence “We did not perform eye-blink artefact removal because it has been shown that blink artefacts are successfully ignored by multivariate classifiers as long as they are not systematically different between decoded conditions (Grootswagers et al. , 2017). ” We also added the following explanations and the figures to the manuscript in the Results section to cover this point. “Although eye-movements should not drive the classifiers due to our design (see Materials and methods), it is still important to verify that the results replicate when standard artefact removal is applied. We can also use eye-movement data as an additional measure, examining blinks, saccades and fixations for effects of our attention and vigilance manipulations. First, to make sure that our neural decoding results replicate after eye-related artefact removal, we repeated our analyses on the data after eye-artefact removal (see Materials and methods), which provided analogous results to the original analysis (see the decoding results without and with artefact removal in Figure 3—figure supplement 2). Specifically, for our crucial distance to object data, the main effects of Attention and Time on Task and the key interaction between and Time on Task remain after eye-artefact removal, replicating our initial pattern of results. Second, we conducted a post-hoc analysis to explore whether eye movement data showed the same patterns of vigilance decrements and therefore could explain our decoding results. We extracted the proportion of eye blinks, saccades and fixations per trial as well as the duration of those fixations from the eye-tracking data for correct trials (-100 to 1400 ms aligned to the stimulus onset time), and statistically compared them across our critical conditions (Figure 3—figure supplement 3). [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of AI prediction related to behavioral outcomes and the necessity for artifact removal in eye-tracking data analysis. It highlights that even after removing eye artifacts, the AI's replication of results does not guarantee that it can prevent health issues, as the focus is on behavioral prediction rather than health prevention. This indicates that while AI can assist in research, it cannot predict or prevent all health issues due to inherent variability and limitations in the data.


Input: Task: pass the claim using the given text. Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: Saccharomyces is a yeast that feeds on sugars and can be found on fruit, such as on the skin of grapes. Interestingly, this yeast has been domesticated to make the species Saccharomyces cerevisiae, commonly known as brewers’ or bakers’ yeast. Propionibacterium are present in a wide variety of environments. Alan and his team are most interested in are those found in dairy products such as cheese. Ultimately, Alan and his team are interested in finding a means of overcoming our reliance on fossil fuels and are therefore investigating microbes as an alternative. “In our projects, Saccharomyces is used for the production of ethanol, whether that be for wine or for biofuels,” explains Alan. “Propionibacterium makes propionic acid, which can be used to keep yeast fermentations free from bacterial contamination. ” ’? The most obvious challenge is the production of ethanol by yeast. The hand gels we use in hospitals all contain ethanol, which helps to kill microbes – the ethanol breaks down the thin membrane around cells and kills them. This is something that Alan and his team are battling with. “When the yeast produces the ethanol, it is killed at an alcohol content of 15%-16%, which explains why wine doesn’t ferment to a higher concentration,” explains Alan. “We would like to have yeast that is able to tolerate higher concentrations, both for the production of biofuels in a more cost-effective manner and also to add into fermentations, such as the secondary fermentation that champagne undergoes in the bottle. ” ’? Yes! The team has been working on a variety of different stresses that affect the cell membrane and they now understand the effects that ethanol and butanol, another type of alcohol, have on membranes at the molecular level. This allows the team to design strains that have the potential to be more resistant – these will be tested later this year, which could lead to some exciting results in the near future. ? The short answer is soon and everywhere! points out, for decades, microbes were used to produce compounds such as butanol before fossil fuels took over. In many ways, the work of biochemists is to get back to where we once were, but the only way of doing that is to match the effectiveness and efficiency of fossil fuels. [SEP] Justify:

Output: The text discusses the use of yeast and other microbes in production processes and the team's efforts to enhance their properties, but it does not address the claim about engineered organisms completely replacing natural species. It focuses more on the applications of these organisms rather than their potential to replace natural counterparts.


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 supports the claim by discussing the use of organic amendments in farming, specifically mentioning composting and manure components like poultry droppings (PD) in the context of organic soil amendments for vegetable production. It highlights a composted mix of BSG and PD as beneficial for addressing nutrient deficiencies in organic farming, which aligns with the description of organic farming practices relying 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: We saw strong evidence (BF = 4. 8e8) for a difference in the number of eye blinks between attention conditions: There were more eye blinks for the Unattended (distractor) than Attended (potentially targets) colour dots. We also observed moderate evidence (BF = 3. 4) for difference between the number of fixations, with more fixations in Unattended vs. Attended conditions. These suggest that there are systematic differences in the number of eye blinks and fixations due to our attentional manipulation, consistent with previous observations showing that the frequency of eye blinks can be affected by the level of attentional recruitment (Nakano et al. 2013). However, there was either insufficient evidence (0. 3 < BF < 3) or moderate or strong evidence for no differences (0. 1 < BF < 0. 3 and BF < 0. 3, respectively) between the number of eye blinks and saccades across our Active, Monitoring, Early and Late blocks, where we observed our ‘vigilance decrement’ effects in decoding. Therefore, this suggests that the main vigilance decrement effects in decoding, which were evident as an interaction between Target frequency (Active vs. Monitoring) and Time on the task (Early vs. Late) (Figure 3), were not driven by eye movements. ” Relatedly, on page 16 the authors claim that "If the prediction from the MEG decoding was stronger than that of the eye tracking, it would mean that there was information in the neural signal over and above any artefact associated with eye movement. " This statement is problematic: Firstly, such a result might only mean that prediction from MEG decoding is stronger than decoding from eye-movements, but not relate to "artefacts" in general, to which blinks would also count. Secondly, given that the signal underlying both analyses is entirely different (and the number of features), it is not valid to directly compare the results between these analyses. More detailed analyses of fixations and fixation duration on targets and distractors might indeed be strongly related to behaviour. What is decodable at a given time might just be driven by what participants are looking at. [SEP] Justify:

Output: The text contrasts the claim by discussing evidence from attentional manipulation studies and prediction analyses that show limitations in predicting outcomes based solely on attentional factors. It highlights that predictions based on different neural and eye-tracking data cannot be directly compared, which implies a limitation in the predictive capabilities of AI in health contexts. This suggests that the ability to predict comprehensive health issues transcends current methodologies and the results associated with attentional mechanisms.


Input: Task: pass the claim using the given text. Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: “One consideration is cost but reducing toxicity should help with this and as people become increasingly environmentally conscious this should help drive government policy,” explains Alan. “There is a lot of amazing work going on to get microbes to make chemicals they wouldn’t normally make, and this broadens their uses significantly. ” in BiochemistryProteins and Lipids (AMPL): : Alan’s work is focused on investigating Saccharomyces and Propionibacteria with a view to improving the tolerance of microbes. The team hopes to improve efficiency and product yield of engineered cell-based factories. : UK Research and 2020 Marie Skłodowska-Curie actions Download the 10. 33424/50 Link to the activity sheet 0 +1 Pinterest 0 LinkedIn 0 cells viewed under a microscope. Yeast grown on an agar plate ready for testing. Testing yeast for ethanol tolerance. A molecular model of a cell membrane showing the individual lipids (fats) that make it up. The MeMBrane team at a recent meeting in York. We are all aware of the potentially cataclysmic effect of continuing to rely on fossil fuels. While we might recycle our plastic, use electric cars or walk to school, efforts are being made to reduce our reliance in surprising quarters. One area where efficiency and a move away from fossil fuels is being considered is that of the use of microbes. Fossil fuel-based chemical production has been a historical tendency for some time and, while it has helped advance the field of biochemistry, it has had the unfortunate effect of releasing enormous amounts of carbon into the atmosphere. As we all now well know, these ‘non-renewable’ production methods are affecting the world’s climate. Now, there is a concerted effort to move away from such methods and instead look for alternatives that are cleaner, greener and more efficient, including biofuels. Based at in the UK, Goddard leads the MeMBrane project, which focuses on the production of what he and his team call cell factories. “Microbial cells can be grown on a variety of ‘foods’ including sugars, plant waste and even wood pulp,” explains Alan. “These sources of carbon are renewable and therefore more environmentally friendly. Bio-based routes to chemical production can also lead to purer products. [SEP] Justify:

Output: The text does not directly address the claim regarding the potential for engineered organisms to replace natural species. Instead, it discusses the efforts in microbial research and the shift towards more environmentally friendly production methods, which may involve engineered cells but does not assert that they could completely replace natural species.


Input: Task: corroborate the claim using the given text. Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: in through your institution issuesearch term Advance 10. 1093/tas/txae109 Published: 01 August 2024 10. 1093/tas/txae109 Select format. ris (Mendeley, Papers, Zotero). enw (EndNote). bibtex (BibTex). txt (Medlars, RefWorks) Download citation issuesearch term issuesearch term 1. 0, a product containing bromoform, was fed to cattle to evaluate its effects on methane (CH4) and carbon dioxide (CO2) emissions and diet digestibility. Twelve nonlactating, nonpregnant Jersey cows (490 ± 19 kg body weight) were used in four replicated 3 × 3 Latin squares with three periods, each consisting of 21 d. Cows were blocked by feed intake (averaged intakes over 4 wk prior to trial) and assigned randomly to one of three treatments. Treatments included Alga 1. 0 fed at 0, 69, and 103 g/d in a 0. 454 kg/d dry matter (DM) top-dress daily in a modified distillers grains plus solubles (MDGS) carrier. Diet consisted of 60% dry-rolled corn, 20% corn silage, 15% modified distillers grains, and 5% supplement (DM basis). Headbox-style indirect calorimeters were utilized to evaluate gas production from individual cows with two nonconsecutive 23-h collections in each period. Data were analyzed using the procedure of SAS with cow within square as experimental unit and as a random effect, and treatment and period as fixed effects. Linear and quadratic contrasts were used to compare treatments. 1. 0 linearly reduced dry matter intake (DMI, P < 0. 01) by 10. 1% for 69 g/d inclusion and 13. 3% for 103 g/d inclusion compared to the control. Nutrient intakes decreased linearly (P < 0. 01) due to lower DMI, but nutrient digestibility was not impacted (P ≥ 0. 28). Inclusion of Alga 1. 0 did not impact gross energy or digestible energy concentration of the diets expressed as Mcal/kg DM (P ≥ 0. 22) but did linearly reduce energy intake (Mcal/d; P < 0. 01). 1. [SEP] Justify:

Output: The text corroborates the claim by discussing a study where a product containing bromoform, presumably derived from seaweed, was fed to cattle to evaluate its effects on methane emissions. It mentions the specific treatments and methodologies used to analyze gas production, reinforcing the potential for certain seaweed-derived feed additives to help reduce methane emissions in livestock.


Input: Task: contrast the claim using the given text. Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: We have added the following paragraph to the Materials and methods section to clarify the point: “Note that the ‘direction of approach’ and ‘distance to object’ information cannot be directly compared on an analogous platform as the two types of information are defined differently. There are also different number of classes in decoding for the two types of information: only two classes for the direction information (left vs. right), compared to the 15 classes for the distance information (15 distances). ” 4. The distance classifier was trained using only correct trials. Then in the testing stage, it was generalized to either correct or miss trials. While there is a rationale for using correct trials only, could the decoding of error prediction be an artifact of the training sample, reflecting the fact that misses were not included in the training set? No, we do not think there is any way it could be an artefact. Our hypothesis is that correct trials contain information which is missing from miss trials. In other words, miss trials are in some way different from correct trials. Thus, it is crucial to use only correct trials in the training set. Please note that our approach is different from most conventional studies in which people directly discriminate correct and miss trials by feeding both types of trials to classifiers in the training phase and test the classifiers on the left-out correct and miss trials (i. e. , without any feature extraction; as in Bode and Stahl, 2014). While this standard approach might lead to a higher classification performance, we developed our new approach for two main reasons. First, in the real world and many vigilance studies, there is usually not enough miss data to train classifiers. Second, we wanted to directly test whether the neural representations of correct trials contain some information which is (on average) less observable in miss trials. The result of conventional methods can reflect general differences between correct and miss trials (i. e. , general level of attention, not time-locked to stimulus presentation), but cannot inform us about whether the difference reflects changes in information coding in the correct vs. miss trials; our approach allows this more specific inference. In our approach, we trained our classifiers on correct trials and tested them on both correct and miss trials. Crucially, we tested the trained classifiers only on unseen data for both correct and miss trials. Specifically, when testing the classifiers, we used only the correct trials which were not used in the training phase. [SEP] Justify:

Output: The text contrasts with the claim by providing detailed information on the limitations of a specific AI approach to classifying correct and miss trials. It highlights that while the AI method developed may enhance understanding of neural representations in trials, it does not imply comprehensive predictive capabilities for all health issues. The discussion about using only correct trials to train classifiers suggests that this approach has specific constraints and does not address the claim that AI can predict and prevent all health issues universally.


Input: Task: pass the claim using the given text. Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: ” – a biological membrane that separates the interior of all cells from the outside environment – a living thing that is too small to be seen with the naked eye YEAST – a group of single-celled fungi that may reproduce by budding, i. e. when a bud detaches and forms into a new fungus – a gram-positive, anaerobic, rod-shaped genus of bacteria named for their unique metabolism – a field of science that involves redesigning organisms for useful purposes by engineering them to have new abilities – an area of chemistry focused on designing products and processes that minimise or eliminate the use and generation of hazardous substances – a renewable energy source, made from organic matter or wastes, that can play a valuable role in reducing carbon dioxide emissions ’? Alan and his team work with strains of Saccharomyces and Propionibacteria. Saccharomyces is a yeast that feeds on sugars and can be found on fruit, such as on the skin of grapes. Interestingly, this yeast has been domesticated to make the species Saccharomyces cerevisiae, commonly known as brewers’ or bakers’ yeast. Propionibacterium are present in a wide variety of environments. Alan and his team are most interested in are those found in dairy products such as cheese. Ultimately, Alan and his team are interested in finding a means of overcoming our reliance on fossil fuels and are therefore investigating microbes as an alternative. “In our projects, Saccharomyces is used for the production of ethanol, whether that be for wine or for biofuels,” explains Alan. “Propionibacterium makes propionic acid, which can be used to keep yeast fermentations free from bacterial contamination. ” ’? The most obvious challenge is the production of ethanol by yeast. The hand gels we use in hospitals all contain ethanol, which helps to kill microbes – the ethanol breaks down the thin membrane around cells and kills them. This is something that Alan and his team are battling with. “When the yeast produces the ethanol, it is killed at an alcohol content of 15%-16%, which explains why wine doesn’t ferment to a higher concentration,” explains Alan. [SEP] Justify:

Output: The text does not directly address the claim regarding the replacement of natural species by engineered organisms. Instead, it discusses Alan and his team's work with yeast and bacteria for the production of ethanol and their investigation of microbes as an alternative to fossil fuels, without mentioning the potential to replace natural species.


Input: Task: corroborate the claim using the given text. Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: 0 linearly reduced enteric CH4 emissions measured as g/kg DMI (P < 0. 01) by 39 and 64% for 69 g/d and 103 g/d inclusion, respectively. Linear reductions (P < 0. 01) of 64% to 65% were also observed in enteric CH4 emissions when expressed per kilogram of DM or organic matter digested. Respired CO2 as g/d linearly decreased (P = 0. 03) for cattle fed Alga 1. 0 but did not differ when expressed as g/kg of DMI (P ≥ 0. 23). Oxygen consumption did not differ between treatments for g/d and g/kg DMI (P ≥ 0. 19). In conclusion, feeding Alga 1. 0 reduced DMI up to 13. 3%, did not impact digestibility, and significantly reduced CH4 emissions up to 63%. Greenhouse gas (GHG) emissions, specifically methane, from the livestock sector have been a focal point of ruminant (cattle) research over the last decade due to the intrinsic nature for ruminant animals to emit methane during digestion and ruminal fermentation. One proposed method to reduce methane emission from the cattle sector is by feeding an algae feed product to cattle. Therefore, the aim of this experiment was to evaluate the effects of feeding varying inclusions of algae in a typical feedlot finishing diet on methane emission. When including algae as a feed additive, cattle methane production was reduced up to 65%. gas (GHG) emissions are a concern related to present and future climate and global warming potential. Methane (CH4), produced via enteric fermentation, is a GHG of interest to the agricultural industry due to its climate warming potential. Since the half-life of CH4 is relatively short, spending an average of 10 to 12 yr in the atmosphere, reducing CH4 emissions will have a positive short-term impact on climate (Allen, 2016). Reducing CH4 production from ruminants can be beneficial for the environment by lessening the emission impact on global warming, reducing the impact of animal production on GHG emissions, and using GWP* suggests reduction of CH4 by cattle would have rapid impacts. As of recent, there has been interest in using algae as a strategy to reduce enteric CH4 emissions from ruminants (Roque et al. , 2019, 2021). [SEP] Justify:

Output: The text corroborates the claim by providing empirical evidence that feeding cattle an algae feed product (referred to as 'Alga 1.0') significantly reduces methane (CH4) emissions, with reductions noted as high as 65%. The text discusses the positive impact of including algae as a feed additive on enteric methane emissions, further emphasizing the potential of algae in mitigating greenhouse gas emissions from livestock.