Input: Task: contrast the claim using the given text. Claim: All coral reefs can be fully restored through artificial means. [SEP] Text: Exploring the transient dynamics of coral populations can thus enhance our understanding regarding the future diversity, condition, and resilience, of coral communities. Globally, coral communities are being exposed to increasingly recurrent thermal stress events, at frequencies soon expected to exceed the recovery time required by most coral species (Hughes et al. 2018; Sully et al. 2019). Presently, considerable value is placed on classifying coral species according to their morphological and functional traits, with the subsequent categorisation of coral taxa offering proxies for the vulnerability of various coral communities to said recurrent stress events (Darling et al. 2012, 2019). Quantifying the relative affinities of different coral populations for demographic recovery presents the opportunity for justifying these assumptions; helping to distinguish the species and populations most vulnerable and or resilient to projected recurrent disturbance regimes. Equally, the amplification and attenuation characteristics of populations simultaneously reflect their stability following a disturbance. Distinguishing between the differential attributes of compensation and resistance in different populations is important for managing the future condition of biological communities (Capdevila et al. 2020). Assessments of coral communities following repeated disturbances have demonstrated divergent shifts in species compositions (Adjeroud et al. 2009; Pratchett et al. 2011, 2020). With knowledge of the capacity for various coral populations to undergo demographic amplification or for avoiding demographic attenuation, we can begin to forecast and anticipate the reassembly of coral communities, thereby improving the effectiveness of adaptive reef management. Indeed, Sandin and McNamara (2012) and Brito-Millán et al. (2019b) demonstrate how the transient characteristics of coral populations determine the resilience of their wider reef communities to algal phase shifts. Importantly, these contributions to our understanding of transient dynamics within coral environments advocate for a consideration of spatial interactions within assessments of coral population dynamics. Spatial interactions between individual corals impacts upon their fitness characteristics (Buss and Jackson 1979; Idjadi and Karlson 2007; McWilliam et al. 2018), yet the exact role these spatial interactions play in defining the performance of coral populations remains unresolved (Alvarez-Noriega et al. 2018). Ultimately, assessing the combined influence of state-structured demography and spatial interactions (e. g. Adler et al. 2010; Chu and Adler 2015) upon the performance, and resilience, of coral populations represents an important and worthwhile endeavour. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities and challenges related to coral populations and their recovery from disturbances. It highlights factors such as thermal stress events and the varying resilience of coral species, indicating that not all coral populations can effectively recover from stress. The text also emphasizes the importance of understanding transient dynamics and the different responses of coral communities to disturbances, indicating that restoration efforts cannot guarantee complete recovery for all reefs.


Input: Task: pass the claim using the given text. Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Therefore, the higher accumulation of l-Phe in the strain producing the highest l-Phe level, and the subsequent 2-PE obtained, may be related to a more efficient channeling of shikimate to l-Phe. The use of agricultural waste products as feedstocks for 2-PE productionTo explore the potential of Pseudomonas putida as a chassis to produce 2-PE from 2G substrates, the capability of producing 2-PE from corn stover (PCS) and sugar cane straw (PSCS) hydrolysates, prepared as described in Materials and Methods was assayed. To this end, P. putida CM12-5 (pPE-1), CM12-5(pPHE1) (pPE-1), CM12-5(pPHE1)-FPA (pPE-1) and CM12-5Δgcd (xylABE) (pPE-1) strains were grown in M8 minimal medium with KNO3 as the N source, and 3% (w/v) PCS or PSCS. 2-PE production levels were determined after 24 h and the results are shown in Table 2. All the strains produced between 82 and 110 mg L−1 2-PE, either from 3% PCS or 3% PSCS hydrolysates. The concentration of sugars was also considered in molar terms to estimate molar yields, which were in the range of 8. 6–11. 1% regarding glucose utilization (Table 2). Table 2 2-PE production from 2G lignocellulosic hydrolysates PCS and PSCS in Pseudomonas putida CM12-5 derivative strainsFull size tableSimilar assays were also done with all of these 2-PE producing strains in M9 minimal medium, but using 1G substrates as the C source, such as corn syrup, which was used at 1/80 (v/v). Our results are shown in Table 3. Surprisingly, the total production of 2-PE with 1G substrates was lower than with 2G substrates, since these strains accumulated between 10 and 84 mg L−1 of 2-PE and the molar yields were below 4. 8%. [SEP] Justify:

Output: The text discusses various strains of Pseudomonas putida and their ability to produce 2-PE from lignocellulosic hydrolysates, but it does not mention bioengineered yeast or specifically address the claim about biofuels being produced from sugars by yeast. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: in Finance and Economics 2023, Issue 4: 354-379. doi: 10. 3934/DSFE. 2023021 Previous 1. Centre for & Informatics, University, Potchefstroom, 2531, 2. for Theoretical and (NITheCS), Received: 16 June 2023 Revised: 14 August 2023 Accepted: 05 September 2023 Published: 13 October 2023 Text(HTML) the realm of machine learning, where data-driven insights guide decision-making, addressing the challenges posed by class imbalance in datasets has emerged as a crucial concern. The effectiveness of classification algorithms hinges not only on their intrinsic capabilities but also on their adaptability to uneven class distributions, a common issue encountered across diverse domains. This study delves into the intricate interplay between varying class imbalance levels and the performance of ten distinct classification models, unravelling the critical impact of this imbalance on the landscape of predictive analytics. Results showed that random forest (RF) and decision tree (DT) models outperformed others, exhibiting robustness to class imbalance. Logistic regression (LR), stochastic gradient descent classifier (SGDC) and naïve Bayes (NB) models struggled with imbalanced datasets. Adaptive boosting (ADA), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and k-nearest neighbour (kNN) models improved with balanced data. Adaptive synthetic sampling () yielded more reliable predictions than the under-sampling (UNDER) technique. This study provides insights for practitioners and researchers dealing with imbalanced datasets, guiding model selection and data balancing techniques. RF and DT models demonstrate superior performance, while and NB models have limitations. By leveraging the strengths of RF and DT models and addressing class imbalance, classification performance in imbalanced datasets can be enhanced. This study enriches credit risk modelling literature by revealing how class imbalance impacts default probability estimation. The research deepens our understanding of class imbalance's critical role in predictive analytics. Serving as a roadmap for practitioners and researchers dealing with imbalanced data, the findings guide model selection and data balancing strategies, enhancing classification performance despite class imbalance. Keywords: credit risk, class imbalance, default prediction, supervised learning, classification Citation: Lindani Dube, Tanja Verster. Enhancing classification performance in imbalanced datasets: A comparative analysis of machine learning models[J]. [SEP] Justify:

Output: The text corroborates the claim by discussing machine learning models in the context of finance, particularly in addressing class imbalance issues and guiding decision-making in predictive analytics. The mention of various classification algorithms, including random forest (RF) and decision tree (DT) models, supports the idea that machine learning is applicable in the finance domain.


Input: Task: contrast the claim using the given text. Claim: All coral reefs can be fully restored through artificial means. [SEP] Text: Alternatively, a disturbance does not necessarily imply a negative impact, merely a change to a system (Pickett and White 1985). Disturbances to the structure of populations can be brought about by the cessation of a continuous pressure (e. g. Götz et al. 2008), changes in resource availability (e. g. Goldstein and Steiner 2020), or their introduction into a novel environment (Jelbert et al. 2019). With the ability of natural populations for undergoing demographic compensation underpinning their capacity for exploiting changes in their local environment (Jelbert et al. 2019), the transient dynamics of populations can offer insight into their invasive potential (Iles et al. 2016). Demographic compensation mediates the persistence of coral populations established along coastlines characterised by increased abiotic variability (Cant et al. 2021a, b). Predicting and quantifying demographic compensation within coral populations will, therefore, aid in forecasting the winners and losers in reef communities targeted by conservation and restoration initiatives, or those undergoing climate induced range shifts. To illustrate this potential, we provide an example evaluating the performance of a subtropical coral population in (Box 4). Box 4 Transient dynamics in Pocillopora aliciae: an empirical exampleFull size tableFig. 5Describing how size mediated patterns in survival, growth, fragmentation, and recruitment, determine the transition of individual colonies during the time interval t to t + 1, we used an IPM to evaluate the condition and viability of a Pocillopora aliciae population. Repeated surveys of individually tagged colonies were used to document the survival, growth, fragmentation, and recruitment characteristics of individuals colonies, and how these patterns are influenced by colony size. By condensing these individual-level patterns into a probability density kernel our IPM subsequently translates the transition of P. aliciae individuals across a continuous size spectrum into measures of both asymptotic and transient population characteristicsFull size imageFig. 6The asymptotic and transient dynamics of Pocillopora aliciae within the forecast very different outlooks for this subtropical population. [SEP] Justify:

Output: The text contrasts the claim by discussing how disturbances to coral populations can lead to changes in their system, suggesting that restoration may not be fully achievable through artificial means alone. It highlights that natural populations can adapt to changes in their environment and that demographic compensation is crucial for the persistence of coral populations. This implies that while artificial restoration efforts may aid in conservation, they cannot guarantee the full restoration of all coral reefs.


Input: Task: pass the claim using the given text. Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Production of 2-PE can be considered to take place in two sets of reactions: one that leads to synthesis of the primary substrate, L-Phe, and a second set that uses the Ehrlich pathway to produce 2-PE. It was found that, whether intracellularly made or exogenously supplied, L-Phe is stoichiometrically converted on 2-PE. The genes of the Ehrlich pathway were expressed in pPE-1 plasmid from the regulated Pm promoter [50, 51], whose expression is driven by XylS in the presence of alkylbenzoates in plasmid pPE-1, or from a constitutively expressed lac promoter in plasmid pPE-2 (not shown). Our unpublished results revealed that the regulated expression of the Ehrlich pathway yielded more consistent production than the constitutive expression, probably due to the generation of instability, as production decreased with time (P. Godoy, unpublished results). The limiting factor in the 2-PE production process was linked to the production of the primary substrate, phenylalanine (L-Phe). The limited flux toward the shikimate synthesis pathway seems to be the main bottleneck for the production of aromatic chemicals, because it is highly regulated at transcriptional and enzymatic levels [25, 52]. A key feature in a wide range of l-phenylalanine producers is the need to overcome the feedback inhibition by l-Phe of the bifunctional chorismate mutase/prephenate dehydratase (PheA) enzyme [24, 25]. This fact has been confirmed in this study, as the expression of the PheAfbr variant lacking the R-domain expressed in plasmid pPHE1 [24] led to significant increases in L-phenylalanine accumulation in the medium. Another step is to increase the availability of shikimate precursors phosphoenolpyruvate (PEP) and erythrose-4-phosphate (E4P) [25, 53,54,55,56,57]. This study shows that xylose is a less efficient C-source for l-Phe production than glucose, which is probably related to the low availability of PEP in cells growing on xylose, which limits the C flow towards the shikimate pathway. [SEP] Justify:

Output: The text does not address the claim that bioengineered yeast can produce biofuels from sugars. Instead, it focuses on the synthesis pathways of L-Phe and 2-PE, discussing their production processes and regulatory factors without mentioning biofuels or yeast. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: in Finance and Economics, 2023, 3(4): 354-379. doi: 10. 3934/DSFE. 2023021 Related Papers: the realm of machine learning, where data-driven insights guide decision-making, addressing the challenges posed by class imbalance in datasets has emerged as a crucial concern. The effectiveness of classification algorithms hinges not only on their intrinsic capabilities but also on their adaptability to uneven class distributions, a common issue encountered across diverse domains. This study delves into the intricate interplay between varying class imbalance levels and the performance of ten distinct classification models, unravelling the critical impact of this imbalance on the landscape of predictive analytics. Results showed that random forest (RF) and decision tree (DT) models outperformed others, exhibiting robustness to class imbalance. Logistic regression (LR), stochastic gradient descent classifier (SGDC) and naïve Bayes (NB) models struggled with imbalanced datasets. Adaptive boosting (ADA), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and k-nearest neighbour (kNN) models improved with balanced data. Adaptive synthetic sampling () yielded more reliable predictions than the under-sampling (UNDER) technique. This study provides insights for practitioners and researchers dealing with imbalanced datasets, guiding model selection and data balancing techniques. RF and DT models demonstrate superior performance, while and NB models have limitations. By leveraging the strengths of RF and DT models and addressing class imbalance, classification performance in imbalanced datasets can be enhanced. This study enriches credit risk modelling literature by revealing how class imbalance impacts default probability estimation. The research deepens our understanding of class imbalance's critical role in predictive analytics. Serving as a roadmap for practitioners and researchers dealing with imbalanced data, the findings guide model selection and data balancing strategies, enhancing classification performance despite class imbalance. 47. doi: 10. 31449/inf. v47i1. 4519 [2] (2022) Sentiment analysis on twitter data integrating textblob and deep learning models: The case of us airline industry. Knowl-255: 109780. doi: 10. 1016/j. knosys. 2022. 109780 [3] (2012) The'k'in k-fold cross validation. in 'ESANN', 441–446. [SEP] Justify:

Output: The text corroborates the claim by discussing the application of machine learning in finance and economics. It mentions the importance of data-driven insights in decision-making, particularly regarding classification algorithms and their performance amidst varying class imbalances, which is a key concern in these domains.


Input: Task: contrast the claim using the given text. Claim: All coral reefs can be fully restored through artificial means. [SEP] Text: 2012) within open-source programs like R (R 2019). Forecasting the resilience of coral populations presents a major challenge for the future conservation of various reef communities (Lam et al. 2020). Currently, we lack the mechanistic insight into the resilience and response of coral populations to disturbance necessary for predicting the future reassembly and persistence of coral communities given increasingly recurrent disturbances. It is not our intention to dismiss previous efforts to evaluate the dynamics of coral populations using asymptotic characteristics. The asymptotic characteristics of populations present a useful tool in exploring the relative tolerances of differing populations (Beissinger and Westphal 1998) and have proven valuable in the management of threatened populations (Johnson et al. 2010). We have illustrated, however, that for evaluating and forecasting the responses of coral assemblages to disturbance and changing environments, asymptotic characteristics do not represent measures of population resilience (Hastings et al. 2018). Instead, we advocate for greater a focus towards investigating the transient dynamics of coral populations that more accurately reflect their dynamics within natural environments (Ezard et al. 2010). Evaluating coral reef community resilience using the framework of transient demographic theory will provide a new effective lens on the temporal and spatial nature of coral reef resilience, a view that will transcend current approaches of conducting single reef assessments to determine resilience (Maynard et al. 2015; McLeod et al. 2021). [SEP] Justify:

Output: The text contrasts the claim by discussing the challenges in forecasting the resilience of coral populations and indicates that there is a lack of mechanistic insight necessary for predicting the future reassembly and persistence of coral communities. It emphasizes that asymptotic characteristics of populations are not adequate for evaluating resilience, suggesting that simple restoration methods may not suffice without a deeper understanding of coral dynamics. Thus, it argues against the notion that all coral reefs can be fully restored solely through artificial means.


Input: Task: pass the claim using the given text. Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: In addition, while the Ehrlich pathway intermediates are stoichiometrically transformed into 2-PE, we observed that part of shikimate made in the cells may divert to dead-end 3-hydroxyshikimate. This was indeed confirmed when clones able to produce higher amounts of L-Phe were isolated upon a round of random chemical mutagenesis that resulted in the identification of a mutation in shikimate dehydrogenase that prevents 3-hydroxyshikimate accumulation, and limited metabolism through the phenylacetic acid degradation pathway. Lignocellulose from agricultural residues or from urban organic waste are expected to represent a relevant raw material for the production of chemicals. Thus, P. putida DOT-T1E and the l-Phe overproducing strains were engineered to bear the machinery involved in xylose uptake and catabolism and also for the 2-PE production genes. However, low production of l-Phe and 2-PE from xylose was found, in spite xylose being metabolized through the pentose phosphate pathway that generates E4P, which is a precursor of the shikimate pathway (see Fig. 1). Due to the fact that when l-Phe or Ehrlich pathway intermediates were added to cells grown of xylose, they were stoichiometrically converted to 2-PE by CM12-5 derivatives; channeling xylose to shikimate seems to be a key limiting step in 2G conversion of C5 sugars into 2-PE. Furthermore, PEP is also limiting due to its channeling to pyruvate that is eventually transformed into acetyl-CoA and enters the TCA cycle [25, 56, 57]. Taking all these facts into consideration, it is clear that it is necessary to re-organize the PEP-4EP metabolic processes when xylose is used as the C-source. It is known that 2G hydrolysates, in addition to sugars, contain a wide range of toxic chemicals such as furfural, 5-hydroxymethylfurfural and others ([30] and Additional file 4: Table S3). These toxic compounds are originated from partial degradation of sugars during steam-explosion in the intensive physicochemical pre-treatment of lignocellulosic material [30]. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses the metabolic pathways and limitations related to the production of specific compounds like 2-PE and L-Phe from xylose. While it touches on xylose, a sugar, and mentions the production processes, it does not provide evidence or direct support that bioengineered yeast can produce biofuels specifically from sugars.


Input: Task: corroborate the claim using the given text. Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: [4] Bentéjac C, Csörgő A, Martínez-Muñoz G (2021) A comparative analysis of gradient boosting algorithms. Rev 54: 1937–1967. doi: 10. 1007/s10462-020-09896-5 [5] (2015) Performance-weighted ensembles of random forests for predicting price impact. 15: 1823–1835. doi: 10. 1080/14697688. 2014. 983539 [6] (2021) A survey of machine learning in credit risk. J 17. = 3946261 [7] (2001) Random forests. Mach learn 45: 5–32. doi: 10. 1023/A:1010933404324 [8] (1984) Classification and regression trees (wadsworth, belmont, ca). 13: 978–0412048418. [9] (2015) Indoor localization in a hospital environment using random forest classifiers. Appl 42: 125–134. doi: 10. 1016/j. eswa. 2014. 07. 042 [10] (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. J Res 11: 2079–2107. [11] Chen T, (2016) Xgboost: A scalable tree boosting system, in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785–794. [12] Chicco D, (2020) The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC Genomics 21: 1–13. doi: 10. 1186/s12864-019-6413-7 [13] (2018) Handling data irregularities in classification: Foundations, trends, and future challenges. 81: 674–693. doi: 10. 1016/j. patcog. 2018. 03. 008 [14] (2011) Bayesian networks classifiers for gene-expression data, in 2011 11th on Design and Applications, IEEE, 1200–1206. [15] (2018) Agricultural drought risk evaluation based on an optimized comprehensive index system. Sustainability 10: 3465. doi: 10. [SEP] Justify:

Output: The text corroborates the claim as it contains references to various studies related to machine learning applications. Specifically, it mentions 'Indoor localization in a hospital environment using random forest classifiers,' which demonstrates machine learning's application in healthcare. Additionally, it references 'A survey of machine learning in credit risk,' indicating its use in finance. These instances illustrate the versatility of machine learning models in numerous sectors.


Input: Task: contrast the claim using the given text. Claim: Most plastic in the ocean can be collected and recycled effectively. [SEP] Text: . org 2024 in to your account Login or this option if you have an account on informs. org or if you want to become an member Combine personal and institutional access with an Informs PubsOnline account using main navigation6, 2018 in Innovative Education: Recycling ocean plastic into the supply chain Student project: University of Michigan’s grad students team up with to address global problem : Share onShare on PRINT 10. 1287/orms. 2018. 04. 13 In sponsoring a 2017 student team project at the University of Institute for Global Operations, presented the challenge of developing an Asia-based ocean plastics supply chain for product packaging, designed to significantly reduce the total landed cost of ocean plastic by 73 percent over the current state. Dell has been an active sponsor of Tauber team projects since 1999, and is represented on the Tauber Institute’s Board by Piyush Bhargava, its vice president for global operations. team for this project consisted of grad students Dan Partin, who is working on Master of and Master of Environment and Sustainability degree, and Allison Ward, a member of the Program (EGL), which leads to BSE in Engineering and MSE in Industrial and degrees. The team’s challenge: identify and justify the best market for ocean plastic in Asia Dell’s packaging among its global markets. is one of the largest privately held global companies that provide a broad range of technology products for the consumer, education, enterprise and government sectors. In addition to its line of desktop and notebook PCs, Dell offers network servers, data storage systems, printers, Ethernet switches and peripherals such as displays and projectors. With the recent acquisition of is also now one of the biggest storage and enterprise services companies in the world. The supply chain is structured to deliver a holistic view of collection, resulting in reduced flow into the ocean. Source: Based in Austin, Texas, Dell has a rich history of supply chain and packaging innovation. As part of its commitment to ocean health outlined at the 2017 Ocean Conference, Dell pledged to source and incorporate 10 times its current annual ocean plastic usage into its packaging, amounting to up to 160,000 pounds annually, by 2025. More than 8 million tons of plastic enters the ocean each year, a growth rate that by 2050 would result in more plastic in the ocean than fish. [SEP] Justify:

Output: The text contrasts with the claim by highlighting that 'more than 8 million tons of plastic enters the ocean each year,' suggesting a significant volume of plastic pollution. It implies that while there may be initiatives to recycle ocean plastic, the sheer amount of plastic that continues to enter the ocean indicates that effective collection and recycling of most plastic is not feasible.


Input: Task: pass the claim using the given text. Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: The introduction of a mutant variant of chorismate mutase/prephenate dehydratase, bypassed feedback inhibition by aromatic amino acids, and significantly enhanced l-phenylalanine production and, consequently, boosted 2-PE yields. Furthermore, the application of random mutagenesis led to strains exhibiting elevated l-phenylalanine production, resulting in increased 2-PE production. In the realm of industrial applications, we explored the use of agricultural waste substrates such as corn stover, sugar cane straw, and corn syrup as potential carbon sources. Regardless of the waste source, glucose was the preferred sugar for 2-PE biosynthesis. To sum up, our study underscores the potential of microbial biorefineries, particularly those employing P. putida, as an environmentally sustainable alternative for producing valuable aromatic compounds like 2-PE. The implementation of strain engineering and the utilization of agricultural waste substrates are pivotal strategies identified for enhancing production efficiency and mitigating environmental impact. Materials and methodsChemicalsl-Phenylalanine (99%), 2-phenylethanol (99%), phenylacetaldehyde (90%), shikimic acid (99%), sodium phenylpyruvate (powder), chorismic acid barium salt (≥ 80%), o-toluic acid (99%), m-toluic acid (99%), p-fluoro-dl-phenylalanine (FPA) (98%), ViscozymeR l-cellulolytic enzyme mixture and ethylmethanesulfonate (EMS) were purchased from Sigma-Aldrich (Merck). Acetonitrile (HPLC grade), d-( +)-xylose and d-( +)-glucose were provided by VWR Chemicals (France). Strains, plasmids and growth conditionsStrains and plasmids used in this study are shown in Table 4. Pseudomonas DOT-T1E is a solvent-tolerant strain that was originally isolated from a wastewater treatment plant [8]. Pseudomonas putida CM12-5 is a DOT-T1E derivative that produced phenylalanine and it was previously described [24]. To make P. putida CM12-5 able to use xylose as a C-source, a derivative of this strain, bearing and xylE genes from E. [SEP] Justify:

Output: The text does not directly address the claim regarding bioengineered yeast producing biofuels from sugars. Instead, it discusses the production of l-phenylalanine and 2-phenylethanol by utilizing various substrates, including agricultural waste, without specifically linking this to the production of biofuels by bioengineered yeast.


Input: Task: corroborate the claim using the given text. Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: 3390/su10103465 [16] (2019) Extreme gradient boosting machine learning algorithm for safe auto insurance operations, in 2019 IEEE international conference on vehicular electronics and safety (ICVES), IEEE, 1–5. [17] (2018) Catboost: gradient boosting with categorical features support. arXiv preprint. doi: 10. 48550/arXiv. 1810. 11363 [18] (1992) The attribute selection problem in decision tree generation, in 'AAAI', 104–110. [19] (2021) Dynamically weighted balanced loss: class imbalanced learning and confidence calibration of deep neural networks. 33: 2940–2951. [20] Granström D, (2019) Loan default prediction using supervised machine learning algorithms. [21] (2012) Data mining concepts and techniques third edition, University of Illinois at Urbana-. [22] Ho TK (1995) Random decision forests, in Proceedings of 3rd international conference on document analysis and recognition, IEEE, 1: 278–282. [23] Kaggle (2023) Give me some credit. Available from: = cs-training. csv. Accessed: 2023-02-05. [24] (2017) Lightgbm: A highly efficient gradient boosting decision tree. Syst 30. [25] 'arcy A (2020) Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies, MIT press. [26] Khemakhem S, (2018) Predicting credit risk on the basis of financial and non-financial variables and data mining. Financ 17: 316–340. doi: 10. 1108/RAF-07-2017-0143 [27] (2015) Software defect prediction using ensemble learning on selected features. Tech 58: 388–402. doi: 10. 1016/j. infsof. 2014. 07. 005 [28] (2019) Machine learning in banking risk management: A literature review. Risks 7: 29. doi: 10. 3390/risks7010029 [29] (2022) Analysis and visualization of accidents severity based on lightgbm-tpe. Chaos, 157: 111987. doi: 10. 1016/j. chaos. 2022. [SEP] Justify:

Output: The text corroborates the claim by providing multiple examples of machine learning applications across different fields such as auto insurance operations and loan default prediction. Furthermore, it mentions applications in banking risk management, clearly indicating that machine learning models are indeed utilized in both healthcare and finance sectors.


Input: Task: contrast the claim using the given text. Claim: Most plastic in the ocean can be collected and recycled effectively. [SEP] Text: We’d do that by extending the supply chain from Haiti to South Asia. ” Lessons learned along the way In addition to the obvious advantage of doing significant work at a top-tier company while still enrolled as students, Tauber team members indicated they learned the following lessons during the project: opened their eyes to the extent of the ocean plastics problem. It was very humbling to meet the informal collectors, and it put their heart behind the project even more. Ocean plastic goes beyond just beach clean-ups. The root cause is unmanaged waste infrastructure, which needs to be developed to fully address the problem. Many stakeholders, both internal and external, were affected by the supply chain. It was important to collaborate and gather perspectives from all parties. Addressing ocean plastic not only has an environmental impact, but also creates economic opportunities in Indonesia and India for collectors, processors and manufacturers. Dell is truly behind the ocean plastic cause and catalyzing industry change. The project goes beyond Dell by creating a working group to dramatically increase the impact on ocean health. The outcome: a new cost-effective supply chain and $350,000+ savings in one year. team’s proposed supply chain delivers scalability with the production capacity to source nearly 500 times Dell’s commitment of ocean plastic usage, or the equivalent of 1. 8 billion plastic bottles annually. It also reduced the total landed cost of ocean plastic by 73 percent over the current state, and by 31 percent and 16 percent against prevailing recycled and virgin plastic prices, respectively. Here are some of the ways the Tauber team reached these goals: Developed a scalable and cost-effective supply chain to source ocean plastic from Southeast Asia, reducing raw material costs up to 73 percent while diversifying the supplier network. Reduced manufacturing costs by 51 percent with a sourcing allocation strategy, allowing Dell to effectively remove the equivalent of 3. 6 million plastic bottles from the ocean annually. Collaborated with key stakeholders to analyze and recommend potential next use cases for Dell to exceed a public goal by 700 percent, totaling 1. 1 million pounds of recycled plastic annually. Tauber grad students (left) and defined a cost-effective supply chain for ocean plastic. Source: The scope of work described in the project had its challenges. Faculty advisor pointed out that, “Generally, operations projects begin with known inputs and a tried-and-true supply chain. [SEP] Justify:

Output: The text contrasts the claim by highlighting that the ocean plastics problem is complex and requires significant infrastructure development to address unmanaged waste effectively. While it describes efforts to create a supply chain for ocean plastic and the economic benefits of tackling the issue, it implies that merely collecting and recycling most ocean plastic is insufficient without addressing the root causes of pollution.


Input: Task: pass the claim using the given text. Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: coli that transports xylose into the cell and the xylAB, genes that encode enzymes to convert xylose into xylose-5 phosphate, which is metabolized through the pentose phosphate pathway, was constructed [45]. Efficient utilization of xylose required the inactivation of the glucose/xylose dehydrogenase (Gcd) to avoid the misrouting of xylose to dead-end xylonate [33]. A derivative of CM12-5 with Δgcd and bearing the xylAB and xylE genes was also available. Escherichia coli DH5α was used for the cloning experiments and propagation of plasmids (Table 4). Table 4 Strains and plasmids used in this studyFull size tableEscherichia coli was routinely grown at 37 ºC in LB medium, while the Pseudomonas putida strains were grown at 30 ºC in LB medium or in M9 minimal medium [60] with 5 g L−1 glucose as the sole carbon source. When indicated M8 minimal medium was used, this is identical to M9 medium except that ammonium chloride was replaced by 10 mM KNO3. For liquid cultures 100 mL conical flasks seeded with 20 mL culture medium were incubated in a Kühner thermostat incubator with agitation (200 rpm, 30 ºC). Growth was monitored by following the turbidity of the cultures at 660 nm (OD660) in a UV–VIS spectrophotometer Shimadzu 1900i (Kyoto, Japan). For resting cells assays, the cells were grown in M9 minimal medium with 5 g L−1 glucose as the sole carbon source, until the culture reached OD660 = 1. 0. Then, the cells were centrifuged and washed with 1xM9 and re-suspended in the appropriate medium at OD660 = 10. Pretreated sugar cane straw (PSCS) and corn stover (PCS) were sourced from the Plant in York, Nebraska, USA. The final dry matter content was determined to be 39. 9% for PSCS and 43. 3% for PCS, as reported in the compositional characterization by Rocha-Martín et al. [30]. [SEP] Justify:

Output: The text does not address the claim regarding bioengineered yeast producing biofuels from sugars. Instead, it discusses the genetic manipulation of Escherichia coli and its growth conditions, along with details about pretreatment of biomass sources, which are not related to the specified claim about yeast.


Input: Task: corroborate the claim using the given text. Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: Abstract Background: The use of intelligent decision support systems (IDSS) is widespread in the healthcare industry, particularly for real-time data, client and family history datasets, and prevalent patient features. Objective: A massive chunk of various kinds of health data sets, including sensor information, medical evidence, and omic statistics, are produced by the modern techniques in this field and eventually transferred to a machine learning (ML) element for extracting data, categorization, as well as mining. Method: In recent times, many patents have been focused on healthcare monitoring; however, they do not adequately incorporate appropriate algorithms for data collection, analysis, and prediction. The data collected is used for predictive modelling, then additionally, machine learning techniques are assisting to compare acquired datasets mathematically for decision-making platforms that may learn to recognise the recent trend and anticipated future problems. Depending on the dataset type, ML-based techniques can assess the circumstances. Training datasets are crucial for correctly anticipating both current and emerging events as well as new challenges. Results: Since the importance of data acquisition determines how well learning models function, any deformed data of the types of dirty data, noisy data, unstructured data, and inadequate information results in inaccurate detection, estimate, and prediction. Conclusion: Additionally, in contrast to other approaches, the experimental findings demonstrate the usefulness of the proposed method as a widespread implementation of machine learning algorithms within healthcare systems. Keywords: Healthcare, machine learning, latency, privacy, communication, intelligent system. 2020. [2] M. Saleem, and J. Sidiq, "Diagnosis and Classification of using Machine Learning-", Syst. Rev. , 2020. [3] H. K. Bhuyan, T. Arun Sai, M. Charan, K. Vignesh Chowdary, and B. Brahma, "Analysis of classification based predicted disease using machine learning and medical things model", 2022 Second international conference on advances in electrical, computing, communication and sustainable technologies (),. April 21-22, 2022, Bhilai, India, 2022. [ [4] K. Adapa, M. Pillai, S. Das, P. Mosaly, and L. Mazur, "Predicting objective performance using perceived cognitive workload data in healthcare professionals: A machine learning study", Stud. Health Technol. Inform. , vol. 290, 2022. [SEP] Justify:

Output: The text corroborates the claim by discussing the widespread application of machine learning (ML) techniques in the healthcare sector, particularly in intelligent decision support systems (IDSS) for analyzing health data. It highlights the role of ML in extracting and categorizing health data, predictive modeling, and addressing emerging challenges in healthcare, which aligns with the assertion that machine learning models are utilized in various fields.


Input: Task: contrast the claim using the given text. Claim: Most plastic in the ocean can be collected and recycled effectively. [SEP] Text: This project had to concern itself with material quality questions, as well as a question of who would supply the ocean plastic. Navigating extra layers of complexity and uncertainty was a hallmark of the student performance and a testament to their determination and skill. ” team project at Dell benefited from exceptional individuals and interpersonal dynamics across the board. Partin and Ward took the challenge of their project and ran with it. “The students had to establish contact and engage themselves, relatively quickly, with a complicated reverse supply chain,” Skerlos adds. “They traveled to China, Indonesia and India, and created meaningful relationships that created fast iteration on supply chain opportunities. ” “Finding ocean plastic was only the first step in the journey,” Ward says. “We knew other important supply chain factors must be taken into consideration, such as cost and continuity of supply, but a key focus of our project was to deliver additionality. This meant we had to demonstrate that Dell’s intervention would make a positive impact on ocean health that would have not otherwise been created. To do so, we created a holistic strategy that would improve the existing waste management structure, targeting the root cause of the problem, not just a symptom. This approach considered the livelihoods and practices of informal sector collectors, local beach clean-ups and community outreach programs, and increased demand for low-value plastic. ” team first defined a cost-effective supply chain for ocean plastic that could be scaled to meet growing demand. Building on previous internal and external research, the team defined viable sourcing locations of ocean plastic based on a variety of factors, including the availability of mismanaged waste, processing infrastructure and logistical simplicity. Onsite visits in validated ocean plastic availability and confirmed the capability of local suppliers to source, clean and process the material. Pricing, capacity and quality certifications were modeled along with logistics, manufacturing costs, taxes and risk factors to define optimal supply chain scenarios for delivering ocean plastic resin to current Dell manufacturing sites in China, as well as scenarios that co-located manufacturing in regions with suppliers. for grad students (left) and onsite in South Asia, where ocean plastic pollution is a growing concern. Source: team went to Shanghai and Xiamen in China, as well as Chennai in India. The team then identified and recommended that Dell certify three Indonesian and Indian partners as viable sources of ocean plastic and co-locate packaging manufacturing in these countries. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities and challenges of collecting and recycling ocean plastic. It highlights the need for a holistic strategy that addresses root causes, logistics, and local practices, rather than simply suggesting that most ocean plastic can be effectively collected and recycled. The mention of establishing a supply chain, costs, and the need for quality certifications indicates that there are significant barriers to effective plastic recycling from the ocean.


Input: Task: pass the claim using the given text. Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Following acid- and steam-explosion pretreatments, the soluble lignocellulosic substrates exhibited furfural contents ranging from 0. 31% to 1. 1%, and 5’-hydroxymethylfurfural contents ranging from 0. 23% to 0. 28%, as documented in the same study (30). PCS and PSCS were then hydrolyzed using (Sigma-Aldrich) as described by Godoy et al. [45]. The sugar content of these hydrolysates was 1. 7 ± 0. 1 g L−1 glucose and 2. 8 ± 0. 1 g L−1 xylose for PSC and 1. 5 ± 0. 1 g L−1 glucose and 1. 4 ± 0. 06 g L−1 xylose for PSCS. The strains were pre-cultured overnight in M8 minimal medium with 10 mM KNO3, as the N source, then, the cultures were diluted at 0. 1 OD660nm in M8 minimal medium with 10 mM KNO3 as N source without glucose and 3% (w/v) acid- and heat- pre-treated corn stover (PCS) or acid- and heat- pre-treated sugar cane straw (PSCS) [30]. Note that nitrate was used in these assays, because the pH of the culture medium remain along the fermentation test above 6. 1, at which growth of P. putida takes place, while when ammonium was the N source, the pH of the culture medium dropped to 4. 5–5. 3, which compromised the survival of the strain. Growth of the strains in this culture was monitored by determining CFU mL−1 in solid M9 medium. Corn syrup was also used as a source of carbon as indicated in the Results section. The corn syrup (purchased from Biosan, Tarragona, Spain) contained about 410 g L−1 glucose, 550 g L−1 fructose and 40 g L−1 sucrose. Isolation of l-phenylalanine overproducer P. putida mutantsputida, l-phenylalanine is produced through the shikimate pathway (Fig. 1). [SEP] Justify:

Output: The text does not provide any information regarding bioengineered yeast or its ability to produce biofuels from sugars. Instead, it discusses various substrates, their sugar content, and growth conditions for certain strains but does not establish a connection to biofuels or the claim about yeast.


Input: Task: corroborate the claim using the given text. Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: [ [PMID: 35673130] [5] S. Verma, R. Popli, H. Kumar, and A. Srivastava, "Classification of thyroid diseases using machine learning frameworks", Int. J. Health Sci. , 2022. [ [6] J. Seo, T. H. Laine, G. Oh, and K. A. Sohn, "EEG-based emotion classification for alzheimer’s disease patients using conventional machine learning and recurrent neural network models", Sensors, vol. 20, no. 24, p. 7212, 2020. [ [PMID: 33339334] [7] L. Eloutouate, F. Elouaai, H. G. Tani, and M. Bouhorma, "Home automation and machine learning models for health monitoring", BDIoT 2021, 2021. [ [9] B. Koval, I. Khlevna, and S. Shabatska, Development of heart disease diagnosis concept using machine learning. , IT&I Workshops, 2021. [10] E. Shakeri, E. A. Mohammed, H. A. Z. Shakeri, and B. H. Far, "Exploring features contributing to the early prediction of sepsis using machine learning. " 43rd Annual international conference of the IEEE engineering in medicine & biology society (EMBC). Nov 01-05, 2021, Mexico, 2021. [11] A. Y. Darveshwala, D. Singh, and Y. Farooqui, "Chronic kidney disease stage identification in hiv infected patients using machine learning", 5th International conference on computing methodologies and communication (ICCMC). April 08-10, 2021, Erode, India, 2021. [12] and N. B. Warif, "Supervised and unsupervised machine learning for cancer classification: Recent development", IEEE international conference on automatic control & intelligent systems (I2CACIS). June 26-26, 2021, Shah Alam, Malaysia, 2021. [13] G. Shilimkar, A. Bhilare, and S. Pisal, "Disease prediction using machine learning", Int. J. Sci. Res. Sci. Technol. , 2021. [ [14] M. F. [SEP] Justify:

Output: The text corroborates the claim by presenting multiple studies and papers that explore the application of machine learning in healthcare, specifically in areas such as thyroid disease classification, emotion classification for Alzheimer's patients, health monitoring, heart disease diagnosis, sepsis prediction, chronic kidney disease identification, and cancer classification. These examples clearly demonstrate the use of machine learning in healthcare contexts, supporting the assertion that machine learning models are indeed utilized in various fields.


Input: Task: contrast the claim using the given text. Claim: Fish populations can recover instantly if fishing is reduced. [SEP] Text: Fisheries‐induced evolution of alternative male life history tactics in Coho salmon Go to 11. Impacts of on Stocks and to 12. Drivers and uncertainties of forecasted range shifts for warm-water fishes under climate and land cover changeKristen L. Bouska, Gregory W. Whitledge, Christopher Lant, and Go to 13. Biochemical evolution in response to intensive harvesting in algae: Evolution of quality and quantity Go to 14. Ecology of harvest‐driven trait changes and implications for ecosystem management Go to 15. Rapid evolution meets invasive species control: the potential for pesticide resistance in sea lampreyErin S. . . Christie, Lori A. Criger, Julia L. M. Hinderer, Robert M. Hollingworth, Nicholas S. Johnson, Stephen R. . Morrison, David Mota-Sanchez, Andrew Muir, Maria S. Sepúlveda, and Michael P. to 16. Limited evolutionary responses to harvesting regime in the intensive production of algae Go to 17. Evaluating the sensitivity of ecological indicators with a perspective of temporal scales Go to 18. Effects of ambient oxygen and size-selective mortality on growth and maturation in guppies Go to 19. Roles of density-dependent growth and life history evolution in accounting for fisheries-induced trait changes Go to 20. Effects of Fishing on the to 21. Illegal tusk harvest and the decline of tusk size in the A frican elephant Go to 22. Vulnerability of individual fish to capture by trawling is influenced by capacity for anaerobic metabolism Go to 23. Sustainable management of freshwater crayfish (kōura, Paranephrops planifrons) in (Rotorua) lakes, North Island, Go to 24. Probabilistic maturation reaction norm trends reveal decreased size and age at maturation in an intensively harvested stock of pikeperch Sander lucioperca Go to 25. Bioeconomic consequences of fishing-induced evolution: a model predicts limited impact on net present valueFabian Zimmermann, Christian Jørgensen, and Go to 26. Statistical power: an important consideration in designing community-based monitoring programs for Arctic and sub-Arctic subsistence fisheries Go to 27. : on the importance of conserving old-growth age structure in fishery populations Go to 28. [SEP] Justify:

Output: The text contrasts the claim by discussing various factors such as fisheries-induced evolution, life history tactics in Coho salmon, and the limited evolutionary responses to harvesting regimes. These points indicate that the recovery of fish populations is influenced by complex evolutionary and ecological factors rather than implying an instant recovery. The mention of 'limited impact on net present value' suggests that reductions in fishing do not guarantee immediate recovery, thereby refuting the idea of instant recovery.


Input: Task: pass the claim using the given text. Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Mutants of CM12-5 and CM12-5 (pPHE1) strains that overproduce l-phenylalanine were generated using ethylmethanesulfonate (EMS) and selection of clones able to grow in the presence of > 3 mg mL−1 p-fluor-dl-phenylalanine (FPA), a toxic analogue of l-phenylalanine [61]. Briefly, cells were grown in M9 minimal medium with glucose until the mid-exponential growth phase was reached. Then, 10 aliquots of 100 μL of liquid culture were spread on M9 minimal medium solid plates with glucose as the C source and 3 mg mL−1 FPA, and left to dry for a few minutes. Then, a filter containing 30 mg of EMS was deposited on each plate and incubated at 30ºC. After 48 h, the colonies appearing within the inhibition halo were considered potential mutants and were picked and tested for growth in plates of M9 minimal medium with glucose and increasing concentrations of FPA (from 3 to 10 mg mL−1 FPA). Those mutants able to grow at high FPA concentrations > 3 mg mL−1 were selected as potential l-phenylalanine overproducers. One clone that tolerated 10 mg mL−1 FPA of each strain, and confirmed to produce l-phenylalanine, was chosen for further analysis. These clones were named PG1E and PG2E, the latter bearing the pPHE1 plasmid. Induction of the expression of Ehrlich pathway genes in pPE-1The construction of pPE-1 plasmid bearing the three genes involved in 2-PE synthesis through the Ehrlich pathway, i. e. histidinol phosphate transferase (PP_0967 from P. putida KT2440), phenylpyruvate decarboxylase (kdc from Rhodospirillum rubrum) and a native alcohol dehydrogenase (T1E_5478, adh from P. putida DOT-T1E) was described by Molina-Santiago et al. [24]. The three genes were cloned as an operon and their expression was driven from the XylS/o-toluic inducible Pm promoter [50] with 1 mM o-toluate. [SEP] Justify:

Output: The text is irrelevant to the claim about bioengineered yeast producing biofuels from sugars. While it discusses the generation of mutants in a specific yeast strain and the expression of certain genes for l-phenylalanine production, it does not provide any information on biofuels or sugar fermentation processes.


Input: Task: corroborate the claim using the given text. Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: Alrifaie, Z. Hussain, A. Shakir, and M. Lafta, "Using machine learning technologies to classify and predict heart disease", Int. J. Adv. Comput. Sci. Appl. , vol. 12, no. 3, p. 12, 2021. [ [15] D. M. Rao, D. R. Mohana, V. Talasila, and M. SureshKumar, "Prediction of chronic diseases at an early phase using machine learning approach", Research gate, 2021. [16] G. Luo, B. L. Stone, and M. D. Johnson, " et al. ,Automating construction of machine learning models with clinical big data: Proposal rationale and methods", JMIR Res. Protoc. , vol. 6, no. 8, p. e175, 2017. [17] G. Feretzakis, A. Sakagianni, and D. Kalles, " et al. , Using machine learning for predicting the hospitalization of emergency department patients", Stud. Health Technol. Inform. , vol. 295, 2022. [ [PMID: 35773897] [18] S. Samet, M. R. Laouar, I. Bendib, and S. Eom, "Analysis and prediction of diabetes disease using machine learning methods", Int. J. Decis. Support Syst. Technol. , vol. 14, no. 1, 2022. [ [19] A. R. Shenoy, and B. R K. , "Performance analysis of class imbalance handling techniques for early sepsis prediction using machine learning algorithms", 8th on and Systems (ICSSS), April 21-22, 2022, Chennai, India, 2019. [21] T. Ragupathi, and M. Govindarajan, "Performance assessment of different machine learning algorithms for medical decision support systems", Proceeding of the international conference on computer networks, big data and iot. Dec 19-20, 2019, Madurai, India, 2019. [22] T. Murari, L. Prathiba, and K. Kumar Singamaneni, " et al. , Big data analytics with oenn based clinical decision support system", & Soft Computing, vol. 31 2022, no. 2, 2021. [SEP] Justify:

Output: The text corroborates the claim by providing references to multiple studies in healthcare that utilize machine learning technologies for classification, prediction, and decision support related to various medical conditions. It specifically mentions using machine learning for predicting heart disease, chronic diseases, hospitalization rates, and diabetes analysis, which directly supports the idea that machine learning is applied in healthcare.


Input: Task: pass the claim using the given text. Claim: Fish populations can recover instantly if fishing is reduced. [SEP] Text: Create a new account user Can't sign in? Forgot your username? Enter your email address below and we will send you your username Email* the address matches an existing account you will receive an email with instructions to retrieve your username close | or * Password* Forgot password? Reset it here Keep me logged in All fields with * are mandatory Email* All fields with * are mandatory Already have an account? Login here password must have 8 characters or more and contain 3 of the following: a lower case character, an upper case character, a special character or a digit password has been changed Can't sign in? Forgot your password? Enter your email address below and we will send you the reset instructions Email* the address matches an existing account you will receive an email with instructions to reset your password the verification code Cancel Congrats! has been verified close 10. 1139/F10-168 Get AccessContentsof Fisheries and Aquatic Sciences, Number 3March 20110. 128 of that for a population composed entirely of wild fish. The effect of hatchery fish on reproductive performance was the same among all three species. Further, the impact of hatchery fish from “wild type” hatchery broodstocks was no less adverse than hatchery fish from traditional, domesticated broodstocks. We also found no support for the hypothesis that a population's reproductive performance was affected by the length of exposure to hatchery fish. In most cases, measures that minimize the interactions between wild and hatchery fish will be the best long-term conservation strategy for wild populations. RésuméNous observons une relation négative entre la performance reproductive de populations naturelles de truites arc-en-ciel anadromes (Oncorhynchus mykiss), de saumons coho (O. kisutch) et de saumons chinook (O. tshawytscha) et la proportion de poissons de pisciculture dans les populations de reproducteurs. Nous utilisons la productivité intrinsèque estimée en ajustant une variété de modèles de recrutement aux données d'abondance de chaque population comme indice de performance reproductive. [SEP] Justify:

Output: The text is irrelevant to the claim since it discusses various aspects of fish populations, particularly regarding the impact of hatchery fish on reproductive performance, without addressing the recovery of fish populations in relation to reduced fishing. Thus, it does not offer any supporting or contrasting information relevant to the claim.


Input: Task: pass the claim using the given text. Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Analytical methodsl-Phenylalanine, 2-phenylethanol (2-PE), and other aromatic intermediates present in culture supernatants were identified and quantified using an elution gradient protocol, with an Agilent 1260 HPLC–DAD system (see Godoy et al. [45]). l-Phenylalanine and 2-PE were both monitored at 215 nm, and their elution times were 5 and 10 min, respectively. Glucose, fructose and xylose consumptions were analyzed enzymatically as described by Godoy et al. [45]. Bio-informatic analysisputida PG2E genome was sequenced by Lda (Caparica, Portugal and (Madrid)). Single-nucleotide polymorphisms (SNPs) were identified by comparing sequencing reads from the PG2E strain against the P. putida DOT-T1E reference genome (GCA_026183455. 1) with Snippy v4. 6. 0 [62] that uses (0. 7. 17) [63] and Freebayes (v1. 3. 6) [64] with parameters, mincov = 10, minqual = 100 and mapqual = 60. Structural variants were analyzed using sorted bam files with Samtools v. 1. 16. 1 [65] and inspected using IGV v2. 15. 2 [66]. Availability of data and materials The data sets generated or analyzed during this study are included in this published 2001. 2020. ET. Ernst T. Fragrance chemistry : the science of the sense of smell. Academic Press; 1982. of biotransformation conditions for production of 2-phenylethanol by a Saccharomyces cerevisiae CWY132 mutant. Res. 2011;25:754–9. BMC Biotechnol. 2014. BMC Biotechnol. 2012;12:1–12. Value added products from fermentation of sugars derived from agro-food residues. Sci Technol. 2017;71:52–64. The future of petrochemicals—analysis - IEA. IEA, Paris License: CC BY 4. 0. 2018. $759 billion required for a net-zero petrochemicals sector by 2050 | BloombergNEF. BNEF. 2022. D, Gielen D. [SEP] Justify:

Output: The text does not address the claim directly. It discusses analytical methods and data regarding various substances like l-Phenylalanine and 2-phenylethanol, but does not provide any information supporting or contradicting the ability of bioengineered yeast to produce biofuels from sugars.


Input: Task: corroborate the claim using the given text. Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: [ [24] A. Karthikeyan, A. Garg, P. K. Vinod, and U. D. Priyakumar, "Machine learning based clinical decision support system for early COVID-19 mortality prediction", Front. Public Health, vol. 9, p. 626697, 2021. [ [PMID: 34055710] [25] M. R. Afrash, M. Yaghoubi, F. Rahimi, M. Shanbehzadeh, and M. Bahadori, "An intelligent system for prediction of severity of sarscov-2 infection and progression to critical illness: Using machine learning models", Researchgate. [26] R. Moore, K. R. Archer, and L. Choi, "Statistical and machine learning models for classification of human wear and delivery days in accelerometry data", Sensors, vol. 21, no. 8, p. 2726, 2021. [ [PMID: 33924388] [27] H. H. Alalawi, "Detection of cardiovascular disease using machine learning classification models", IJERT, vol. 10, no. 7, 2021. [28] M. Ganesan, and N. R. Sivakumar, "IoT based heart disease prediction and diagnosis model for healthcare using machine learning models", IEEE international conference on system, computation, automation and networking (). . March 29-30, 2019, Pondicherry, India, 2019. [ [29] S. Mishra, K. Shaw, and D. Mishra, "et al. , Improving the accuracy of ensemble machine learning classification models using a novel bit-fusion algorithm for healthcare ai systems", Front. Public Health, vol. 10, p. 858282, 2022. [ [PMID: 35602150] [30] A. Nikam, S. Bhandari, A. Mhaske, and S. Mantri, "Cardiovascular disease prediction using machine learning models",. Dec 16-18, 2020, Pune, India, 2020. [ [31] H. Mattie, P. J. Reidy, P. Bachtiger, E. R. Lindemer, M. Jouni, and T. [SEP] Justify:

Output: The text corroborates the claim as it includes multiple references to the application of machine learning models in healthcare settings, such as 'Machine learning based clinical decision support system for early COVID-19 mortality prediction' and 'Detection of cardiovascular disease using machine learning classification models.' This clearly indicates that machine learning is employed in healthcare, thus supporting the claim.


Input: Task: contrast the claim using the given text. Claim: Fish populations can recover instantly if fishing is reduced. [SEP] Text: L'importance de cette relation négative est telle que nous prédisons que la performance du recrutement dans une population composée entièrement de poissons de pisciculture serait de 0,128 celle d'une population composée totalement de poissons sauvages. L'effet des poissons de pisciculture est le même chez les trois espèces. De plus, l'impact des poissons de pisciculture provenant de stocks reproducteurs de pisciculture de « type sauvage » n'est pas moins négatif que celui des poissons de pisciculture de stocks reproducteurs traditionnels et domestiqués. Nous ne trouvons aucun appui à l'hypothèse qui veut que la performance reproductive d'une population soit affectée par la durée de son exposition aux poissons de pisciculture. Dans la plupart des cas, les mesures qui minimisent les interactions entre les poissons sauvages et les poissons de pisciculture constituent la meilleure stratégie de conservation à long terme pour les populations sauvages. Get full access to this 2008. Fitness of hatchery-reared salmonids fish in the wild. Evol. Appl. , 1(2): 342–355. CrossrefWeb of Science2004. Review of the relative fitness of hatchery and natural salmon. U. S. Dept. Commer. ,. No. NMFS-NFSC-61. 2004. The controversy about salmon hatcheries. Fisheries (Bethesda, Md. ), 29: 12–31. CrossrefWeb of Science2009. Using an unplanned experiment to evaluate the effects of hatcheries and environmental variation on threatened populations of wild salmon. Biol. Conserv. 142(11): 2449–2455. CrossrefWeb of Science2002. Model selection and multimodel inference: a practical information-theoretic approach. Springer-Verlag, New York, N. Y. 1996. Status review of west coast steelhead from Washington, Idaho, Oregon, and California. U. S. Dept. Commer. ,. NMFS-NWFSC-27. 2009. [SEP] Justify:

Output: The text contrasts the claim by discussing the negative impacts of hatchery-reared fish on wild populations, suggesting that recovery of fish populations isn't instantaneous and that effective conservation involves minimizing interactions between wild and hatchery fish. It highlights the complexity of population dynamics, indicating that immediate recovery from reduced fishing pressures is unlikely.


Input: Task: pass the claim using the given text. Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Zero-emission pathway for the global chemical and petrochemical sector. Energies. 2021;14:3772. 2021. Available from: H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. 2013. Available from: E, Marth G. Haplotype-based variant detection from short-read sequencing. 2012. Available from: The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9. 24. 75. 00. Author informationAuthors and AffiliationsDepartment of Environmental Protection, Estación Experimental del Zaidín, CSIC, c/ 1, 1808, & Juan L. RamosDepartment of Biomedical Informatics, University of Arkansas for 72205, author publicationsYou can also search for this author in PubMed 1. Comparative growth of P. putida DOT-T1E, CM12-5 and PG2E strains in the presence of glucose or phenylacetate as C source. Growth of the three P. putida strains was tested in M9 minimal medium with 0. 5% (w/v) glucose or 10 mM phenylacetate as C-source. Turbidity (right side) in liquid medium was measured 24 h after inoculation, while growth on solid medium was observed after 24 h incubation of streaked cells on solid M9 medium with either glucose or phenylacetate. Additional file 2: Table S1. SNPs found in P. putida PG2E genome with respect to P. putida DOT-T1E. Additional file 3: Table S2. Identification of mutated genes related with L-phenylalanine metabolism in P. putida PG2E as compared to P. putida DOT-T1E strain. Additional file 4: Table S3. Chemical composition of the soluble fraction of acid- and steam- explosion pretreated corn stover (PCS) and sugar cane straw (PSCS). Data are taken from Rocha-Martin et al. [30]. Rights and permissions This 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the licence, and indicate if changes were made. [SEP] Justify:

Output: The text provided does not address the claim regarding bioengineered yeast and its ability to produce biofuels from sugars. The content focuses on various studies and data related to the growth of certain strains of bacteria (Pseudomonas putida) and their metabolic processes, as well as some data on chemical compositions of plant materials. There is no mention of bioengineered yeast or biofuels, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: Panch, "A framework for predicting impactability of healthcare interventions using machine learning methods, administrative claims, sociodemographic and app generated data", Arxiv, 2019. & Patents on Engineering Title:in Healthcare using Models Volume: 18 Issue: 5 Author(s): Anup Patnaik*K. Affiliation: Institute of Engineering and : Healthcare, machine learning, latency, privacy, communication, intelligent system. Abstract: Background: The use of intelligent decision support systems (IDSS) is widespread in the healthcare industry, particularly for real-time data, client and family history datasets, and prevalent patient features. Objective: A massive chunk of various kinds of health data sets, including sensor information, medical evidence, and omic statistics, are produced by the modern techniques in this field and eventually transferred to a machine learning (ML) element for extracting data, categorization, as well as mining. Method: In recent times, many patents have been focused on healthcare monitoring; however, they do not adequately incorporate appropriate algorithms for data collection, analysis, and prediction. The data collected is used for predictive modelling, then additionally, machine learning techniques are assisting to compare acquired datasets mathematically for decision-making platforms that may learn to recognise the recent trend and anticipated future problems. Depending on the dataset type, ML-based techniques can assess the circumstances. Training datasets are crucial for correctly anticipating both current and emerging events as well as new challenges. Results: Since the importance of data acquisition determines how well learning models function, any deformed data of the types of dirty data, noisy data, unstructured data, and inadequate information results in inaccurate detection, estimate, and prediction. Conclusion: Additionally, in contrast to other approaches, the experimental findings demonstrate the usefulness of the proposed method as a widespread implementation of machine learning algorithms within healthcare systems. this page × Export File: RIS (for ) BibTeX Text Content: Citation and this 10. 2174/1872212118666230606145738 DOI Print ISSN1872-2121 2212-4047 this journal Call for Papers in Submission closes on : 03 February, 2025 Processing and The rapid evolution of computational information processing and edge computing has transformed numerous industries, enabling more efficient data processing, real-time analytics, and intelligent decision-making at the edge of networks. This special issue aims to gather cutting-edge research, innovative methodologies, and practical applications in these dynamic fields. We invite contributions that. [SEP] Justify:

Output: The text corroborates the claim by detailing the widespread application of machine learning in the healthcare industry, emphasizing its role in intelligent decision support systems (IDSS) and the processing of vast amounts of health data. It states that machine learning techniques assist in predictive modeling and decision-making platforms, demonstrating its implementation in the healthcare sector.


Input: Task: contrast the claim using the given text. Claim: Fish populations can recover instantly if fishing is reduced. [SEP] Text: One example is by fishing. Fishing selects fish with unique traits. First, fishers take large fish out of the water. So, populations become small and have only small fish left in them. These fish have babies for the first time when they are younger and smaller. Second, different fishing methods can select fish with different behaviors. So, fishers might catch more timid fish or more active fish, depending on the method. Fishing makes fish populations less diverse and productive. Less diverse means there are only small fish or active fish, not a mix of big, small, active, and timid. Less productive means fewer fish to catch later. Small, less diverse populations are weaker and cannot respond to large changes in the environment. Weaker populations are more difficult to fish in a way that will allow the population to survive. So, fishing makes the fish populations less valuable to the fishers. When you go fishing, you hope to catch the big fish. Big fish are better to show off to your friends (and enemies). Professional fishers prefer big fish, too. They get paid more for big fish, and it can be illegal to take small fish. Laws specify size limits to protect small fish, so they can grow and reproduce before fishers take them out. Laws protecting small fish help to make sure that fish stay abundant, so there will still be fish to catch in the future. What is the Although catching big fish seems to be the best thing to do, there are several problems with this strategy. First, protecting small fish does not guarantee that the fish numbers will remain high. Big fish have more offspring (babies) and their offspring are healthier. So, protecting only small fish can lead to smaller fish populations. There is a second problem. If we only remove big fish, we will run out of big fish! Offspring size depends on the size of the parents. Big fish will give birth to babies that can grow to become big fish. Small parents will have small offspring. Over time, the number of big fish in the population will be lower. Think about a cow farm. Farmers prefer cows with their favorite traits. Big, healthy, well-behaved cows that have many babies and give a lot of milk. Farmers will keep their favorite cows and the best bull to reproduce, to make sure they will have cows with the preferred traits year after year. But we are doing the opposite with the fish in the sea! [SEP] Justify:

Output: The text contrasts the claim by explaining that fish populations become less diverse and productive due to fishing, indicating that simply reducing fishing does not guarantee immediate recovery. It highlights how fishing selects for certain traits in fish, leading to weaker populations that struggle to adapt and flourish, thus refuting the idea of instant recovery.


Input: Task: pass the claim using the given text. Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Saccharification of recalcitrant biomass and integration options for lignocellulosic sugars from Catchlight Energy’s sugar process (CLE Sugar) access Published: 28 January 2013 Saccharification of recalcitrant biomass and integration options for lignocellulosic sugars from Catchlight Energy’s sugar process (CLE Sugar) Johnway Gao1, Dwight Anderson1 & Benjamin Levie1 Biotechnology for Biofuels 85. 8% of biomass carbohydrates are saccharified for un-debarked Loblolly pine chips (softwood), and 94. 0% for debarked maple chips (hardwood). Furan compound formation was 1. 29% of biomass feedstock for Loblolly pine and 1. 10% for maple. At 17% solids hydrolysis of pretreated softwood, an enzyme dose of 0. 075 g Sigma enzyme mixture/g dry pretreated (unwashed) biomass was needed to achieve 8. 1% total sugar titer in the hydrolysate and an overall prehydrolysate liquor plus enzymatic hydrolysis conversion yield of 76. 6%. At a much lower enzyme dosage of 0. 044 g CTec2 enzyme product/g dry (unwashed) pretreated softwood, hydrolysis at 17% solids achieved 9. 2% total sugar titer in the hydrolysate with an overall sugar yield of 85. 0% in the combined prehydrolysate liquor and enzymatic hydrolysate. CLE Sugar has been demonstrated to be effective on hardwood and herbaceous biomass, making it truly feedstock flexible. ConclusionsDifferent options exist for integrating lignocellulosic sugar into sugar-using operations. A sugar conversion plant may be adjacent to a CLE Sugar plant, and the CLE Sugar can be concentrated from the initial 10% sugar as needed. Concentrated sugars, however, can be shipped to remote sites such as ethanol plants or other sugar users. In such cases, options for shipping a dense form of sugars include (1) pretreated biomass with enzyme addition, (2) lignocellulosic sugar syrup, and (3) lignocellulosic sugar solid. These could provide the advantage of maximizing the use of existing assets. BackgroundWoody biomass is one of the most abundant biomass feedstocks, besides agriculture residuals in the United States. [SEP] Justify:

Output: The text provides detailed information about saccharification of biomass and integration of lignocellulosic sugars, but it does not address the role of bioengineered yeast or confirm that bioengineered yeast can produce biofuels from those sugars. Therefore, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Biometrics are increasingly used for identity verification. [SEP] Text: Go to Review 5683. DOI: 10. 19080/BBOAJ. 2018. 06. 555683 Go to Review 1823. Fingerprint was first begun to be taken in ink on dactlograms by a by and the concept of fingerprinting has been unique identifier to catch criminals that took off from the Scotland Yard. In 1970's electronic reader was being developed that led to today's technologies, Laboratories has developed hand geometry readers in 1985 and the US government has purchased the readers. Types of biometrics The common biometrics based on physiological or behavioural characteristic that a normal person have are: a. Face b. Iris c. Retina d. Voice e. Handprint f. Fingerprint g. Signature h. DNA pattern i. Sweat pores Basically, the biometric method of identifying a person is preferred over traditional methods. As the use of computer in information technology areas is rapidly increasing, a secure restricted access to privacy or personal data must be obtained. The biometric technique can be used in many application area in order to prevent unauthorized access to use the privacy data such as ATMs, smart card, computer networks, time & attendance system, desktop PCs and workstation. Nowadays, there are several types of system developed with biometrics techniques for real-time identification. The face recognition and fingerprint matching are the most popular biometric techniques used to develop the system followed by hand geometric, iris, retina, and speech. The oldest biometric technique is the electronic fingerprint recognition which has undergone researches and development to extend its applications since its applications by law enforcement. There are two related matching techniques, which are minutiae-based, and correlation- based. What makes a fingerprint unique includes three main patterns; the loop the whorl and the arc using these patterns to match a fingerprint will generally locate a positive identification [4]. The finger print biometric machine is an automated digital version of the old ink and-paper method used for more than a century for identification, primarily by law enforcement agencies. The digital version involves electronically reading users finger print when placed on a platen where the details are mined by the biometric machine algorithm and a finger print pattern analysis is performed. A fingerprint is an impression of the friction ridges on all parts of the finger and pattern recognition algorithms rely on the features found in the impression. [SEP] Justify:

Output: The text corroborates the claim by describing various biometric techniques and their applications in identity verification. It explains how biometric methods like fingerprint recognition are preferred over traditional methods for securing personal data and preventing unauthorized access in modern applications such as ATMs, smart cards, and computer networks.


Input: Task: contrast the claim using the given text. Claim: Fish populations can recover instantly if fishing is reduced. [SEP] Text: If we want the best fish, we should do what farmers do. We should leave the big fish in the sea, so they reproduce. That way, we will always have big fish. Evolution Fishing-induced evolution is a term used by scientists to describe the fact that fishing changes the fish. Fishing affects fish in oceans, seas, lakes, and rivers. Evolution is the change in traits, or characteristics like size, color, or certain behavior patterns, that are passed on from parents to offspring. Evolution happens when two things occur at the same time. First, a trait (for example, size) gives the fish a better chance to survive and have babies. Second, the trait is an heritable trait, which means it can be passed on from parents to their offspring. Fishing can trigger evolution. First, fishing removes the big fish. So, the small fish will have a better chance to survive and reproduce. Second, size is heritable. It passes from parents to offspring through their genes. So, when fishers continually remove the big fish, the smaller fish reproduce and eventually, through evolution, the entire population will consist of smaller fish. for Just Size! Fishing does not only select for fish of a certain size. Fishing can select on other characteristics too [1], for example, maturation and behavior. Keep reading to see how this happens! is an important time in the life of any species. It is the time when the fish change from juveniles to adults and become able to reproduce and produce offspring for the first time. A lot of energy is needed to mature, and the sources of this energy are scarce. So, during the maturation period, a fish needs to grow slower or even stop growing to channel energy to the maturation process. The energetic cost to the fish can even decrease the chances of its survival. The age and size at which a fish is ready to mature depend on two things: the amount of food the fish can obtain from the environment and how the fish shares the food among its important life processes. These life processes include growing, maturing, storing energy, and surviving. These life processes compete for the energy provided by food. The amount of food and the way food is shared between life processes depends on many things, including the temperature of the water, how many predators exist, how much food there is, and how many fish are living together. [SEP] Justify:

Output: The text contrasts the claim by explaining the process of evolution in fish populations. It states that fishing, especially the removal of larger fish, leads to a change in the population's traits. This indicates that recovery is not instantaneous; rather, it requires time for the fish population to adapt and evolve, as smaller fish take over reproductive roles. The information highlights that fish populations experience evolution over time, depending on various factors such as food availability and environmental conditions, contradicting the idea of an instant recovery.


Input: Task: pass the claim using the given text. Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Conventional bisulfite pulping time is as long as 6–10 hours [10], and the calcium bisulfite usage is about 31–35% on wood, equivalent to 9. 7–10. 9 total combined SO2 on wood [11]. Zhu et al. [12] introduced a sulfite pretreatment to overcome recalcitrance of lignocellulose (SPORL) where the effective pretreatment was conducted at a higher temperature of 180°C for 30 minutes with 8–10% bisulfite and 1. 8–3. 7% sulfuric acid on wood. Faster pretreatment time, a bisulfite charge much less than that of bleachable grade bisulfite pulping, and avoiding bleaching and related unit operations downstream makes the cost of pretreatment more consistent with what can be a cost-effective replacement for sugar. In this paper, we introduce CLE Sugar, which begins with a mild bisulfite pretreatment and results in a high yield pretreated feedstock that enables enzymatic hydrolysis of even recalcitrant feedstocks such as softwood. It is operated at time and temperature conditions intermediate between SPORL and conventional bisulfite pulping times, uses less than half the bisulfite chemical as bisulfite pulping, and avoids the addition of any other acid. Thus, the mild bisulfite step lowers the cost of producing highly enzymatically digestible biomass and for subsequent lignocellulosic sugar production. ResultsBiomass feedstocksThe carbohydrate compositions of softwood chips (un-debarked Loblolly pine chips), hardwood chips (debarked maple chips) and switchgrass (Alamo variety) were analyzed. Table 1 below summarizes the carbohydrate composition of the biomass used. The carbohydrate composition of each biomass was determined by converting the polymeric sugars in the feedstock into monomeric sugars such as glucose, xylose, mannose, arabinose and galactose. Results in Table 1 show the original polymeric sugar composition of the biomass. The total polymeric sugar composition for un-debarked Loblolly pine chips, debarked maple chips and switchgrass samples used in the current study was 51. 8%, 58. 6% and 59. 5%, respectively. [SEP] Justify:

Output: The text does not address the claim directly and focuses instead on the bisulfite pulping process and the carbohydrate composition of biomass. It discusses pretreatment methods for lignocellulosic materials without mentioning bioengineered yeast or its role in biofuel production. Hence, it is irrelevant to the claim about bioengineered yeast producing biofuels from sugars.


Input: Task: corroborate the claim using the given text. Claim: Biometrics are increasingly used for identity verification. [SEP] Text: These are sometimes known as «epidermal ridges» which caused by the underlying interface between the dermal papillae of the dermis. Gorman identified three major areas where finger print biometrics are used to include: Law enforcement purposes, fraud prevention in entitlement programs, and physical and computer access generally referred to as large-scale Systems (AFIS). It is believed that no two people have identical fingerprint in world, so the fingerprint verification and identification is most popular way to verify the authenticity or identity of a person wherever the security is a problematic question. The reason for popularity of fingerprint technique is the uniqueness of a person arising from his behaviour and personal characteristics which indicates that each and every fingerprint is unique, different from one other. Finger print recognition is probably the most widely used and well-known biometric that is very less vulnerable to errors in harsh errors environments. Though fingerprint biometrics is applicable in all fields of human endeavour, the finger print of some individuals working in certain industries such as chemical and agricultural are often badly affected making fingerprint mode of authentication problematic. However, to overcome this problem multimodal biometrics system can be applied and fingerprint recognition systems are still considered as the right applications under the right circumstances. Phillips and Martin concluded that finger print biometrics is one of the efficient, secure, cost effective, ease to use technologies for user authentication and according to their survey almost all drawbacks are addressed in fingerprint biometric system. Verification and identification Biometric systems operate in two basic modes: verification and identification [5,6]. Verification mode is used to validate a person against whom they claim to be and use one to one matches by comparing biometric data taken from an individual to a biometric template stored in a database. Verification relies on individuals enrolling on the system and registering their identity prior to providing biometric samples which is a massive administrative task when applied to international border control. Identification mode uses a one to many match and searches all the templates in a database to identify an individual therefore the system does not need the compliance of the data subject. The accuracy of biometric technology depends on the accuracy and number of records within the databases. In the verification mode, the system validates a person’s identity by comparing the captured biometric data with her own biometric template(s) stored system database. [SEP] Justify:

Output: The text corroborates the claim by detailing the use of fingerprint biometrics in various areas such as law enforcement, fraud prevention, and access control, highlighting their effectiveness in identity verification. It explains that fingerprint recognition is one of the most popular and secure biometric methods, supporting the assertion that biometrics are increasingly utilized for verifying identities.


Input: Task: contrast the claim using the given text. Claim: Fish populations can recover instantly if fishing is reduced. [SEP] Text: In general, fish can use one of two life strategies that affect the age at which they mature and the other life processes: • “Live fast, die young” strategy. If the risk of death is high, it is not worth trying to reach old age and energy should be put toward reproduction. For example, when fishers are fishing loads of fish, the amount of fish death is high. In that case, the best strategy is for a fish to put all its energy into maturing quickly and reproducing early, to have offspring before fishers catch them. • “Live slow and grow old” strategy. If the risk of death is low, fish can have a long future waiting for them, and there is no rush to mature or reproduce. The best strategy is to mature and reproduce later in life. These fish can grow larger and have more offspring. Larger fish can live longer, because big fish have fewer natural enemies than small ones. Fishing favors the “live fast, die young” strategy [1]. Fish mature younger and smaller in fished populations. So, fishing affects maturation in two ways. First, fishing leads to more death, so fish must live fast. Second, fishing removes large fish, so the small fish that mature young have more offspring than the bigger fish that mature late. a few scientists study the combination of fish behavior, fishing, and evolution. It is a new idea. However, many researchers have observed that fish in today’s waters mature younger and smaller (Figure 1). Maturation is the first fish trait that we and other researchers studied, because data on fish size and maturation are easy to collect. We also study how fish behavior is related to fishing [3, 4]. We do our experiments in the lab if we cannot do them directly in the sea. We think that we can witness the evolution of fish during our lifetime. This is awesome but also a bit scary! Figure 1 - Examples of fish species that exhibit the “live fast, die young” strategy. Fishing has caused these fish to mature at an earlier age and become smaller in size [1]. The colored markers show where each species lives. Fish images © Sockeye salmon and chum salmon: Timothy Knepp, U. S. Fish and [Public domain]. American plaice and sole: Jonathan Couch, 1877 [2]. Lake whitefish: 1927–1940 [Public domain]. [SEP] Justify:

Output: The text contrasts the claim by explaining the complex life strategies of fish in response to fishing pressures, indicating that fishing alters the maturation age and size of fish populations. It suggests that fishing leads to a 'live fast, die young' strategy where fish mature earlier to reproduce before being caught. This implies that a reduction in fishing may not lead to an instant recovery of fish populations since the altered dynamics, such as earlier maturation and smaller size, would take time to revert. Therefore, the fish populations may not recover instantly, contradicting the claim.


Input: Task: pass the claim using the given text. Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Table 1 Biomass carbohydrate composition Full size tableThe wood chips were re-chipped with a Bearcat garden chipper with a 1. 91 cm screen to obtain smaller size chips. 3-mm round-hole fines were removed to avoid circulation problems in a 28. 3-liter pilot pretreatment reactor. The resulting wood chip size distribution for the softwood chips was 24% “pin size” chips (passes a 7 mm round hole screen, retained on a 3 mm round hole screen), and 76% “accept size” chips (retained on a 7 mm round hole screen). All softwood chips were less than 8 mm thickness. The lengths of the re-chipped chips ranged approximately from 20 to 40 mm. PretreatmentA few biomass feedstocks, including un-debarked Loblolly pine chips, debarked maple chips, and Alamo switchgrass were pretreated in the pilot digester with the mild bisulfite pretreatment method as described in the method section. Table 2 shows the pretreated biomass yield and prehydrolysate amount after the pretreatment. Due to the mild bisulfite pretreatment condition, a high biomass yield was obtained at 74. 9%, 69. 3%, and 81. 6%, respectively for un-debarked Loblolly pine chips, debarked maple chips, and switchgrass. The prehydrolysate has a reducing sugar titer around 2. 6–3. 7% that is mostly hemicellulose sugars. The prehydrolysate sugars account for 15, 18, and 11% of the biomass sugar, respectively for un-debarked Loblolly pine chips, debarked maple chips and switchgrass. In the pretreatment chemistry and mild conditions, the furan compound formation was 1. 29% of biomass feedstock for Loblolly pine and 1. 10% for maple, as shown in Table 3. Table 2 Biomass parameters before and after acidic calcium bisulfite pretreatment Full size tableTable 3 Furan formation Full size tableThe pretreated biomass carbohydrate compositions are shown in Table 4. Table 2 shows the hemicellulose solubilization and hydrolysis to hemicellulosic sugars in the prehydrolysate. These hemicellulosic sugars include arabinose, galactose, xylose and mannose. [SEP] Justify:

Output: The text does not address the claim about bioengineered yeast producing biofuels from sugars. Instead, it focuses on the composition and processing of biomass feedstocks like Loblolly pine chips and maple chips, detailing methods such as pretreatment and sugar yields. As such, it is irrelevant to the specific assertion regarding yeast and biofuels.


Input: Task: corroborate the claim using the given text. Claim: Biometrics are increasingly used for identity verification. [SEP] Text: In such a system, an individual who desires to be recognized claims an identity, usually via a Number (PIN), a user name, a smart card, etc. , and the system conducts a one- to-one comparison to determine whether the claim is true or not (e. g. , "Does this biometric data belong to Janet? "). Identity verification is typically used for positive recognition, where the aim is to prevent multiple people from using the same identity. Identification mode involves establishing a person's identity based only on biometric measurements. The comparator equivalent they obtained biometric with the ones stored in the database bank using a 1: N matching algorithm for recognition. In the identification mode, the system recognizes an individual by searching the templates of all the users in the database for a match. Therefore, the system conducts a one-to-many comparison to establish an individual's identity (or fails if the subject is not enrolled in the system database) without the subject having to claim an identity (e. g. , "Whose biometric data is this? "). Identification is a critical component in negative recognition applications where the system establishes whether the person is who she (implicitly or explicitly) denies to be. The purpose of negative recognition is to prevent a single person from using multiple identities. Identification may also be used in positive recognition for convenience (the user is not required to claim an identity). While traditional methods of personal recognition such as passwords, PINs, keys, and tokens may work for positive recognition, negative recognition can only be established through biometrics [3]. Identified application areas Management is an area that has been carried out using attendance software that relies on passwords for users’ authentication. However, this type of system allows for impersonation since the passwords can be shared or tampered with. Passwords could also be forgotten at times thereby preventing the user from accessing the system [7]. Oloyede et al. [8] carried out extensive research on applicability of biometric technology to solve the problem of staff attendance. However, the researchers did not write any software to address the problems of attendance [9-12]. Marijana carried out a critical review of the extent to which biometric technology has assisted in controlling illegal entry of travelers into specific country through the integration of biometric passport. The issue regarding how the false acceptance rate can be measured in a border control setting was also looked into. [SEP] Justify:

Output: The text corroborates the claim by detailing how biometric technology is utilized for identity verification processes. It explains both positive and negative recognition in biometric systems, highlighting their essential role in establishing and verifying identity. The mention of algorithms for one-to-one and one-to-many comparisons supports the assertion that biometrics are a critical and increasingly adopted method for identity verification.


Input: Task: contrast the claim using the given text. Claim: Fish populations can recover instantly if fishing is reduced. [SEP] Text: We used small trawl nets inside an aquarium and saw that bold fish escaped the nets by being more active and better at swimming [3]. at the As we explained, fish have many traits linked to their living processes. These traits depend on each other and on the amount of energy the fish has. A fish with a “live fast, die young” strategy matures early and has more offspring. But to do so, the fish must grow slower and invest less energy into its defense strategies. This “live fast” fish will be bolder and eat more [5]. So, fishing not only leads to smaller-sized fish. It should also drive changes in many other traits. We are only starting to learn about how fishing affects the evolution of all these new traits. Our plan is to study more traits to see how they are affected by fishing. We will first do our experiments in the lab, because it is easier than in the sea. We want to see how “live fast” fish defend themselves from predators and diseases. We also want to study how much they eat and how they affect the food web. This information will help us to understand the effects of fishing on the whole ecosystem. to Fish Populations? Fishing causes changes in the fished populations. Fishing not only kills fish, but it also tends to leave the remaining fish populations less abundant (fewer fish) and less diverse (fewer different traits). If fishing causes fish to evolve into smaller, less diverse populations, then future generations of fish might be in trouble. We know that small and less diverse populations are weaker because the fish may not be able to deal as well with changes in the environment and may eventually die out. The remaining fish in the evolved population are also less valuable for fishers and are more difficult to catch, which makes fishing less profitable. When an activity has such negative effects on a population of organisms that the population may not recover, we say that the activity is not a sustainable activity. Many of our current fishing methods are not sustainable and may permanently damage fish populations. To save the fish, and to make fishing more sustainable, we need to fish less. We can also reduce the fishing selection, for instance by also actively fishing for medium-sized fish, instead of only taking out the big ones. We can also combine different fishing methods that target different traits to maintain diverse populations. [SEP] Justify:

Output: The text contrasts the claim by explaining that fishing has long-term negative effects on fish populations, leading to smaller, less diverse populations that may not be able to recover. It states that fishing not only kills fish but also alters the genetic traits of the remaining fish, which can result in sustained ecological changes that weaken future generations. It emphasizes that some fishing practices may permanently damage fish populations, indicating that recovery cannot be instantaneous.