Input: Task: contrast the claim using the given text. Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: They insisted keeping a critical eye on the information AI generated as it was not reliable. To do this, students shared similar strategies which was to use ChatGPT as a departure rather than a destination for writing, thinking, and research. They underscored the importance of validation and critical thinking in this process. Another facet to consider is the ethical use of AI. The students believed that one must be very careful when using ChatGPT as it can easily walk the line of plagiarism. They deemed acts such as using ChatGPT to generate new ideas and write entire essays unethical, as these are forms of taking credit for other people’s work based on their language and ideas (Kevin, In-Class Writing). Thus students emphasized the importance of “doing research on your own” (Emma), “making sure the ideas are my own” (Lydia), and “not using everything (i. e. sentence by sentence, word by word) provided by ChatGPT” (Su). The students also regarded the issue of retaining human voice a pivotal consideration for AI-assisted writing. They pointed out that writing should be a means to express the writer’s identity and thoughts, but AI was not able to personalize the text to their individual style and voice. Wary of the threat posed by extensive adoption of ChatGPT to individual expressions, Lydia commented, “ChatGPT tended to use similar dictions and patterns of wording and sentence structures. If everyone uses ChatGPT, our style will become more and more alike” (Lydia, Interview). Similarly, Su pointed out that ChatGPT could make the text “sound generic and impersonal,” which is a problem “when you are trying to convey your own ideas, feelings, and perspectives” (Su, Written Assignment). To “truly present a unique perspective and make writing individualized,” one must “take full control” of their writing to deliver a powerful message (Kevin, Reflection). This process requires the discernment to dismiss advice from ChatGPT to avoid generating an impersonal, blunt style of writing that lacks the writer’s distinct character. [SEP] Justify:

Output: The text contrasts the claim by discussing students' critical views on using AI, specifically ChatGPT, in their writing and research processes. While the claim suggests that AI personalizes learning experiences, the text emphasizes the limitations of AI in maintaining individual writing style and expression. The students express concerns that using ChatGPT could lead to generic and impersonal writing, undermining their unique perspectives and voices. Comments like Lydia’s about the risk of similar diction and writing patterns, as well as Su’s point about the text sounding generic, demonstrate a strong contrast to the idea that AI can effectively personalize learning tailored to individual student needs.


Input: Task: pass the claim using the given text. Claim: Many supposed sightings of cryptids lack verifiable evidence. [SEP] Text: of the B 377(1857):20210378 Fakruhayat M, Rashid A. 2023. Analyzing of global mapping research trends on firefly tourism and fireflies (Coleoptera: Lampyridae) from the year 2000–2022: a literature review and bibliometric analysis. Geografia-of Society and Space 19(1):133-151 érez-Hernández. 2021. Evaluating firefly extinction risk: initial red list assessments for North America. 16(11):e0259379 Faust LF. 2017. Fireflies, glow-worms and lightning bugs. Athens, Georgia: of Georgia Press. 356 Fontaine C, Fontaine B, Prévot AC. 2021. Do amateurs and citizen science fill the gaps left by scientists? in 46(Suppl. 4):83-87 -á L, Martínez Hernández F, Odjo S, Verhulst N. 2023. Review of agronomic research on the milpa, the traditional polyculture system of Mesoamerica. Frontiers in Agronomy 5:1115490 Gardiner T, Didham RK. 2020. Glowing, glowing, gone? Monitoring long-term trends in glow-worm numbers in south-east England. Diversity 13(2):162-174. 2022. The role of community science in entomology. of Entomology 67(1):437-456 Goldin J, Suransky C. 2023. Keep the flow: citizen science as agonistic learning. Citizen Science: Theory and Practice 8(1):1 Gutiérrez-Carranza I, Zaragoza-Caballero S. 2024. Photinus gabicastanoae sp. nov. (Coleoptera: Lampyridae) from Michoacán, Mexico. Dugesiana 31(2):217-221 üller A, Sumser H, Hörren T, Goulson D, de Kroon H. 2017. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. 12(10):e0185809 IFT. 2016. [SEP] Justify:

Output: The text is largely a list of academic references and does not address the claim about cryptids or the evidence related to their sightings. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: In general, these transfers are from archaea that live in similar oxygen and temperature conditions as the bacteria that receive the genes. Potential hotspots of horizontal gene transfer between archaea and bacteria include hot springs, marine sediments, and oil wells. Cold spots for horizontal transfer included dilute, aerobic, mesophilic environments such as marine and freshwater surface waters. gene transfer (HGT) is an essential aspect of microbial evolution, but its mechanisms, rates, and consequences are poorly understood and difficult to quantify (Boto, 2009). Some organisms are more likely to engage in horizontal gene transfer than others. For HGT to occur, the transferred DNA has to be incorporated into the genome after uptake. For this reason, HGT is more common among closely related organisms (Kloesges et al. , 2011). Intra-domain gene transfer seems to have been particularly important in bacterial evolution (Wolf et al. , 2012). However, HGT does occur between archaea and bacteria and is more easily detected in such cases. Since the apparatus for transcription and translation are significantly different between archaea and bacteria, only transferred genes with strong benefits would be retained in their new genome, as their initial expression is likely to be low (Lawrence & Hendrickson, 2003). There is abundant evidence of HGT of key genes between bacteria and archaea adapted to live in similar environments: hot acid (Fütterer et al. , 2004), acid mine drainage (Guo et al. , 2015), extremely high salt (Mongodin et al. , 2005), marine (Frigaard et al. , 2006; Brochier-Armanet et al. , 2011), high mercury (Barkay et al. , 2010), and high temperature (Rhodes et al. , 2011). Whether HGT is limited by geography has not been well investigated, but it likely spans oceans and continents. HGT between archaea and bacteria can be accomplished by acquiring free DNA from the environment, which implies greater proximity between the two parties, or by transfer of DNA from viruses or gene transfer agents. A comparison of viral metagenomes across the world’s oceans revealed that viruses are widely dispersed, though there is a relationship between geographic and genetic distance (Angly et al. , 2006). [SEP] Justify:

Output: The text corroborates the claim by detailing how horizontal gene transfer (HGT) occurs, particularly between archaea and bacteria, and noting that this process can lead to the acquisition of beneficial traits necessary for survival in similar environments. It emphasizes that HGT is an essential aspect of microbial evolution and that it is more effective under certain conditions, which supports the assertion that bacteria can gain new genes and traits through this mechanism.


Input: Task: contrast the claim using the given text. Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: The results suggest that students’ sensemaking of AI-assisted writing is shaped by their prior knowledge and understanding of writing as a cognitive and sociocultural activity, their exploration of AI’s functionalities and strategies for leveraging them to achieve learning goals, and their interrogation of the appropriateness and limitations of AI in the specific context of academic writing. The study highlights two emerging dilemmas students experienced in their generative AI-assisted writing processes. The first dilemma, as Alex put it, is the choice between sounding better or sounding like me when integrating AI into the decision making process of writing, reflecting a larger issue about academic integrity, authenticity, and voice in human-AI collaboration. The participants believed that it is crucial to prevent their writing from being AI-nized, which could lead to either plagiarism or a writing style resembling AI that overshadows their own voice—the very essence of their “identity and presentation of the self in writing” (Prince & Archer, 2014, p. 40). The students’ beliefs align with a connectivism paradigm of AI in Education (AIEd) outlined by Ouyang and Jiao (2021), in which AI serves as a tool to augment human intelligence and capability (Yang et al. , 2021) and learner agency is placed at the core. Reliance on AI could lead to superficial engagement with writing tasks, discouraging deeper, reflective thought processes essential for original creative expression. Furthermore, when AI suggests similar vocabulary, structures, and styles to various learners, it risks imposing a uniformity in expression that undermines the educational value of cultivating each individual’s unique and creative voice. AI may hinder students from exploring how language variation and linguistic diversity can be rich resources for meaning-making, creativity, identity formation, problem-solving (Wang et al. , 2020). Such critical engagement with diverse language resources is crucial for developing students’ literacy skills in a digital age where multicultural awareness is an integral part of education (Sánchez-Martín et al. , 2019). As Dixon-Román et al. (2020) noted, educators must be wary of AI’s “racializing forces,” which standardize learning processes in ways that can marginalize non-dominant forms of knowledge and communication, as well as students whose experiences and identities are either not represented or misrepresented in the system. [SEP] Justify:

Output: The text contrasts the claim by discussing concerns about AI's impact on students' writing processes and their sense of identity. It emphasizes dilemmas students face, including the risk of losing their authentic voice and identity in writing due to AI suggestions, which could lead to a loss of individual expression and originality. It suggests that reliance on AI could impose uniformity and discourage deeper engagement, rather than personalizing learning experiences in a beneficial way.


Input: Task: pass the claim using the given text. Claim: Many supposed sightings of cryptids lack verifiable evidence. [SEP] Text: Informe anual sobre los derechos, riesgos, intereses, preferencias, tendencias o patrones de consumo de los usuarios 2015–2016. Mexico: de Telecomunicaciones. INEGI. 2015. sobre disponibilidad y uso de tecnologías de la información en los hogares, 2015. Aguascalientes: de Estadística y Geografía. Irwin A. 2018. No PhDs needed: how citizen science is transforming research. Nature 562:480-482. 2007. Characterization of firefly habit using a geographical information system with hydrological simulation. 209(2–4):392-400 Koffler S, Barbiéri C, Ghilardi-. 2021. A buzz for sustainability and conservation: the growing potential of citizen science studies on bees. Sustainability 13(2):1-15. 2024. Illuminating firefly diversity: trends, threats and conservation strategies. Insects 15(1):71 ópez‐ópez. 2021. Firefly tourism: advancing a global phenomenon toward a brighter future. and Practice 3(5):24 ák M, López-. 2020. A global perspective on firefly extinction threats. Bioscience 70(2):157-167. 2018. Social media as a platform for a citizen science community of practice. Theory and Practice 3(1):3 Lloyd JE. 1966. Studies on the flash communication system in photinus fireflies. Ann Arbor: Museum of Zoology, University of Michigan. MacGregor-Fors I. 2008. Relation between habitat attributes and bird richness in a western Mexico suburb. Landscape and 84(1):92-98. 2021. Social media use by citizen science projects: characterization and recommendations. Frontiers in 9:1-11 Patterson T, Kelso NV. 2012. World urban areas, LandScan, 1:10 million (2012). [Shapefile]. Society. Pérez-Hernández CX, Gutiérrez Mancillas AM, del-Val E, Mendoza-Cuenca L. 2023. Living on the edge: urban fireflies (Coleoptera, Lampyridae) in Morelia, Michoacán, Mexico. [SEP] Justify:

Output: The text does not address the claim regarding cryptids or their sightings. Instead, it discusses various topics such as citizen science, fireflies, and technological use in households, which are unrelated to the notion of cryptids lacking verifiable evidence.


Input: Task: corroborate the claim using the given text. Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: Though most viruses are specific to either bacteria or archaea, Caudovirales viruses may be able to infect both bacteria and archaea (Prangishvili, Forterre & Garrett, 2006), and little is known about the range of gene transfer agents (McDaniel et al. , 2010). Multiple methods of detecting HGT have indicated that it can be influenced by the lifestyles of the organisms involved. One analysis of recent HGT, as determined by identification of identical 500 nucleotide blocks, indicated that ecology was more important than phylogeny; oxygen tolerance and pathogenicity were identified as the strongest factors (Smillie et al. , 2011). Another study identified several hundred phylogenies of single genes (out of 7,000) that reflected the lifestyles of the organisms rather than their taxonomy (Schliep et al. , 2011). For example, in 597 gene trees, hyperthermophiles and non-hyperthermophiles were better discriminated than were archaea and bacteria (Schliep et al. , 2011). Rhodes et al. (2011) also detected increased transfer between thermophiles. However, when halophilic archaea were examined with the same technique, they appeared to have predominantly exchanged genes with non-halophilic microbes (Rhodes et al. , 2011). It is possible this could be related to an ancient acquisition of 1,000 bacterial genes that caused Haloarchaea to differentiate from methanogens (Nelson-Sathi et al. , 2012). Contrastingly, Gophna et al. (2015) found a negative correlation between growth temperature and gene transfer. In short, evidence indicates that horizontal gene transfer is affected by lifestyle differences, particularly growth temperature, oxygen utilization and pathogenicity. Many similar studies, however, have reported that genome size is the most important factor determining the amount of horizontal gene transfer. In a study utilizing phylogenetic trees to examine HGT in eight genomes, genome size and %GC were the most significant factors determining the number of horizontally transferred genes, rather than more environmentally-relevant factors (Jain et al. , 2003). Presence-absence patterns discordant with species trees have also indicated that large genomes have more horizontally transferred genes than small genomes (Cordero & Hogeweg, 2009; Gophna et al. , 2015). A small genome is an adaptive strategy that allows a microbe to use fewer nutrients and resources. [SEP] Justify:

Output: The text corroborates the claim by discussing the mechanisms and influences on horizontal gene transfer (HGT) which enables bacteria to acquire new traits. It notes that HGT is affected by factors such as lifestyle differences and identifies specific studies that demonstrate how traits related to ecology, such as oxygen tolerance and pathogenicity, are gained through HGT. Additionally, it presents evidence showing the correlation between genome size and the amount of horizontally transferred genes, reinforcing the idea that HGT contributes to the acquisition of various traits in bacteria.


Input: Task: contrast the claim using the given text. Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: We found that some tasks that receptionists routinely perform in one practice were performed by administrators at another practice. The practices we observed differed considerably in their organisational forms, from single-site practices working independently to larger groups or ‘super-practices’ who share services across multiple sites. The issue of ‘task fit’, that is, allocating tasks to the appropriate person, involves both matching the task to the most appropriate pay grade and making sure clinicians are shielded from unnecessary administrative work. The driving force behind the assigning of tasks to occupations in this study was to accurately represent the core identity and ‘scope of work’ of each observed occupation. The third issue is the fundamental difference between ‘clinical’ and ‘administrative’ tasks. There is an abundance of administrative and clerical tasks that occur in primary care. While the field researcher observed, catalogued and categorised nearly every clinical interaction that takes place in the domain of primary care, it was often hard to distinguish administrative tasks from those that are purely clinical. Also, the O*NET database that was used for our analysis provided more terminology and language to describe administrative tasks and office work than it did for nuanced clinical work. ConclusionEvidence from our study show there is great potential to impact workloads in primary care by automating certain administrative tasks (approximately 44%, broadly). We believe that careful introduction of currently available technologies has considerable potential to reform administrative workflows and increase productivity in primary care. However, this should not be seen as a magic bullet that will solve the workload crisis in the immediate future. Introduction of new forms of automation should proceed cautiously with ongoing evaluation of their wider impacts. 2016. Available: Understanding pressures in general practice, 2016. Available: M, Everington S. Tackling the crisis in general practice. BMJ 2016;352:i942. doi:10. 1136/bmj. i942pmid: Engl. General practice forward view, 2016. Available: H, Stern R. Making time in general practice. Foundation, 2015: 1–83. ↵. association survey of GPs in England. London, 2016. ↵ Eighth national GP Worklife survey, 2016. Available: Clinical workload in UK primary care: a retrospective analysis of 100 million consultations in England, 2007-14. Lancet 2016;387:2323–30. doi:10. [SEP] Justify:

Output: The text contrasts the claim by discussing the allocation of administrative tasks in primary care, specifically noting that a significant portion of tasks can be automated. It suggests that while there is potential to reform administrative workflows and increase productivity through automation of administrative tasks, it does not specifically address or corroborate the claim about AI tools in education focusing on grading and attendance.


Input: Task: pass the claim using the given text. Claim: Many supposed sightings of cryptids lack verifiable evidence. [SEP] Text: PeerJ 11(4):e16622 Pérez-Hernández CX, Zaragoza-Caballero S, Romo-Galicia A. 2022. Updated checklist of the fireflies (Coleoptera: Lampyridae) of Mexico. Zootaxa 5092(3):291-317 Center. 2019. Mobile connectivity in emerging economies. Ramírez-Manzano S, Cano-Santana Z, Cibrián-Tovar J, Luna-Cavazos M, Romero-Manzanares A, Ruiz-Posadas L, García-Moya E. 2023. Influence of abiotic factors and vegetation type on the abundance of Photinus palaciosi adults (Coleoptera: Lampyridae) in Nanacamilpa, Tlaxcala, Mexico. de Biodiversidad 94:e945091 R Team. 2017. R: a Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. da Silveira LFL. 2021. A comprehensive review and call for studies on firefly larvae. PeerJ 9(1):1-24 Soga M, Gaston KJ. 2016. Extinction of experience: the loss of human-nature interactions. Frontiers in Ecology and the Environment 14:94-101. 2007. The habitat requirement of the genji-firefly Luciola cruciata (Coleoptera: Lampyridae), a representative endemic species of Japanese rural landscapes. 15:177-189 Theodorou P, Radzevičiūtė. 2020. Urban areas as hotspots for bees and pollination but not a panacea for all insects. 11(1):576. 2021. Global assessment of artificial habitat use by amphibian species. 257(6412):109129 van. 2020. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368(6489):417-420. 2016. Design principles for engaging and retaining virtual citizen scientists. 30:562-570. 2022. Physiological responses of the firefly Pyrocoelia analis (Coleoptera: Lampyridae) to an environmental residue from chemical pesticide imidacloprid. Frontiers in Physiology 13:879216. 2021. [SEP] Justify:

Output: The text is irrelevant to the claim about cryptids and their supposed sightings. It discusses various studies related to fireflies and insects, offering no information about cryptids or evidence supporting or refuting their sightings.


Input: Task: pass the claim using the given text. Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: Consequently, microbes with smaller genomes expel unused genes (Giovannoni et al. , 2005). For example, in a well studied group of archaea, Methanosarcina mazei has the smallest genome of the sequenced Methanosarcina and has clearly shed transferred genes found in other members of the genus (Garushyants, Kazanov & Gelfand, 2015). Gene loss is more common than gene gain over the evolution of both bacteria and archaea (Wolf et al. , 2012). On the other hand, a large genome is an adaptive strategy in which excess genes are retained longer (Chang et al. , 2011) in order to allow greater versatility (Guieysse & Wuertz, 2012). Thus, counting the absolute number of transferred genes present in a genome does not necessarily reflect the rate of horizontal transfer into that organism. This, however, could happen at a much larger scale when comparing organisms from very different environments. In order to compare bacteria from a range of living conditions, we look for bacteria with unusual levels of HGT with archaea than predicted by their genome size. total of 448 bacterial and 57 archaeal genomes (Table S1) were compared using reciprocal BLAST hits (BLAST version 2), where each BLASTp hit had to have an e-value of less than or equal to 1e−10 and the alignment length was >50% of the query (Table S2). Genomes were downloaded from the NCBI ftp site in 2009 and in cases where a microbe had one or more plasmids, those plasmids were included with the genome. All genomes were 100% complete and closed and no two genomes are from the same species. Information about each organism and its temperature, oxygen, and salt p1. code and RBH dataset necessary to reproduce the regression modeling and visualization is freely available at In addition, we examined the Korbel normalization, which includes the genome size of both bacteria and archaea: weighted average = 2 × A × B A 2 + B 2 where A and B are the genome size of the archaeal and bacterial genomes (Korbel et al. , 2002). fit was similar to our log–logistic function, but was found to be less accurate for bacteria with >5,000 genes (Fig. S2). Therefore, we used the log–logistic function in all our analyses. [SEP] Justify:

Output: The text does not directly address the claim regarding horizontal gene transfer (HGT) and its role in enabling bacteria to acquire new traits. Instead, it discusses genome size, gene loss compared to gene gain, and methodological aspects of comparing genomes. This information is tangential and does not specifically corroborate or contrast the claim about HGT.


Input: Task: contrast the claim using the given text. Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: This fusion has resulted in grading systems that not only measure performance but also enhance the learning process by delivering pedagogically sound feedback matched with educational goals, such as increasing critical thinking and problem-solving skills. 2. 5 The current landscape: AI-driven adaptive learning systems2. 5. 1 State-of-the-art AI systems in educationToday’s automated grading landscape is dominated by powerful AI-driven adaptive learning systems. Darvishi discusses the current innovations in this arena, emphasizing how these systems leverage advanced algorithms to offer individualized feedback and adapt to different learning patterns [11]. Capable of identifying learning gaps, personalizing content, and giving tailored interventions, these technologies dramatically increase the learning experience. They reflect the result of years of evolution in educational technology, blending AI breakthroughs, language processing, and educational theory to create a dynamic, responsive learning environment. Advertisement 3. Principles of AI in gradingThis 3. 1 AI algorithms in education3. 1. 1 Core algorithms and their capabilitiesAI algorithms have transformed student assessments in schools. Key algorithms such as machine learning (ML) and deep learning are pivotal in this change. Crompton, Burke, and Jones note that these algorithms excel in processing and learning from big datasets, allowing for extremely accurate evaluation of student replies [12]. They are capable of evaluating patterns in student answers, delivering personalized feedback, and modifying evaluation criteria based on individual student performance. 3. 2 Evolution of AI in grading systems3. 2. 1 Technological advancements in NLP and MLAdvancements in natural language processing (NLP) and machine learning (ML) have greatly pushed the advancement of AI in grading systems. Kompa, Hakim, and Palepu describe how NLP enables AI systems to interpret and analyze human language, which is critical for judging written responses [13]. Meanwhile, ML allows these systems to learn from data, improve over time, and make more accurate assessments regarding the quality and content of student work. 3. 3 Accuracy and reliability of AI grading3. 3. 1 Ensuring fairness and consistencyEnsuring accuracy and reliability is a fundamental priority in AI grading. Patel underlines the necessity for AI systems to be not just efficient but also fair and consistent in their assessments [14]. This involves the constant development of algorithms to decrease errors and biases and validation against broad datasets to assure equitable grading across various student populations. 3. 4 Ethical considerations in AI grading3. 4. [SEP] Justify:

Output: The text contrasts the claim by elaborating on the current advancements in AI-driven adaptive learning systems that go beyond mere administrative automation. It highlights how these systems provide individualized feedback, adapt to learning patterns, and enhance overall learning experiences, emphasizing their role in improving critical thinking and problem-solving abilities. This suggests that AI in education is not limited to automating tasks but is significantly focused on personalizing and enhancing the learning process.


Input: Task: pass the claim using the given text. Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: Main textThe natural history of tobacco dependenceNicotine is among the least rewarding of all addictive drugs: only 20 % of first time users find smoking to be relaxing [1]. Smoking provides an image, a shared social activity, and something to do when bored. These factors motivate smoking until dependence takes over. Prior to the onset of dependence, sporadic non-daily use is typical. The classic withdrawal syndromeThe seminal research on tobacco withdrawal focused on individuals with advanced dependence [2–4], as was the case with research on withdrawal associated with other substances [5, 6]. Because of this history, the term ‘withdrawal’ has traditionally been used to describe the symptoms experienced by individuals with advanced physical dependence [7, 8]. In this report, the historical connotation of the word ‘withdrawal’ is expanded to accommodate recent advances in scientific knowledge afforded by the study of individuals with emerging dependence. The emergence of physical dependenceMany addictive drugs cause a physical (physiologic) dependence that manifests as withdrawal symptoms. [7] (The term physical dependence is introduced here to indicate that the discussion is about to focus on that aspect of dependence related to withdrawal phenomena. The author acknowledges that there are psychological aspects to dependence and that the physical and psychological may be tightly entwined. ) Tobacco withdrawal symptoms are identifiable as such because (1) they recur upon each withdrawal from tobacco, (2) they appear in a characteristic sequence, (3) they appear after a characteristic latency, and (4) they are relieved immediately upon using tobacco. Based on these criteria, it has been shown that physical dependence on tobacco develops through a characteristic sequence of stages in all addicted smokers [9]. As physical dependence begins to develop, the earliest symptom is withdrawal-induced ‘wanting’ of a cigarette [10]. ‘Wanting’, by definition, is mild, short-lived and fairly easy to ignore. It does not intrude upon the patient’s thoughts. For individuals in the first stage of physical dependence, wanting is the only withdrawal symptom experienced. The second stage of physical dependence is characterized by withdrawal-induced ‘craving’. Craving is a more intense and persistent sensation than is wanting, but what distinguishes craving qualitatively from wanting is the fact that craving intrudes upon the patient’s thoughts. Smokers often describe craving as if something inside of their head is telling them that it is time to smoke. [SEP] Justify:

Output: The text is irrelevant to the claim, as it discusses tobacco dependence, withdrawal symptoms, and the characteristics of addictive behavior, without any mention of folklore or cultural perceptions of nature.


Input: Task: corroborate the claim using the given text. Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: , 2006). Sequences were aligned, and distances were calculated with mothur (Schloss et al. , 2009). The purpose of the Mantel test was to test whether deeply-branching bacteria share more genes with archaea than more divergent bacteria. Therefore, for the Mantel test, only the distances between bacteria and the archaeal root were included in order to remove the phylogenetic signal from bacteria-bacteria relationships. The phylogenetic distances between each bacteria and the archaeal root were used to create a bacteria-bacteria Euclidean distance matrix with Primer. test compared a bacteria-bacteria matrix of phylogenetic distance to archaea with a bacteria-bacteria matrix of shared genes with archaea. Mantel tests were performed with the R package “vegan”. DarkHorse algorithm DarkHorse is a statistical method to look for horizontal gene transfer, using lineage probability index ranking scores (LPI), which inversely reflect phylogenetic distance between the query amino acid sequence and its closest database match (Podell & Gaasterland, 2007; Podell, Gaasterland & Allen, 2008). The lineage of each gene is compared to the lineage of the entire organism. The genes flagged by the DarkHorse algorithm have not been checked with phylogenetic trees, so they should be considered potential horizontally transferred genes. However, the phylogenetic distance between bacteria and archaea is very large, so we are examining genes with very low LPI numbers (below 0. 2 (Podell & Gaasterland, 2007)), which increases the robustness of our analysis. Scores at the lowest end of the LPI range are the most likely to be real horizontally transferred genes because their best hit is quite distant from the query organism (Podell & Gaasterland, 2007). Files including LPI numbers for all available proteins in each of the archaeal and the non-pathogenic bacterial genomes were downloaded from the DarkHorse database at at the genus level comparison (Podell, Gaasterland & Allen, 2008). Using a genus level comparison to find transferred genes means that genes shared at a family or class level would not be considered horizontally transferred. This setting may bias our results away from ancient transfer events. For example, the transfer of bacterial genes that allowed the class Haloarchaea to form from methanogens (Nelson-Sathi et al. , 2012) would not be marked as transferred in our scheme. [SEP] Justify:

Output: The text corroborates the claim by describing the DarkHorse algorithm, which is a method used to identify horizontal gene transfer in bacteria. It explains that genes flagged by this algorithm are potential candidates for horizontal transfer, which directly supports the idea that bacteria can acquire new traits through mechanisms such as horizontal gene transfer. Additionally, references to the phylogenetic distance between bacteria and archaea and examples of gene transfers indicate the process through which bacteria indeed acquire new traits.


Input: Task: contrast the claim using the given text. Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: 1 Addressing biases and ethical concernsThe employment of AI in grading brings forward important ethical problems. Goel underlines the significance of facing potential biases in AI systems, which may emerge from biased training data or incorrect algorithm design [15]. It is vital to guarantee AI systems are transparent and their decision-making processes are accessible to educators and students, sustaining trust and fairness in AI-assisted grading. 3. 5 The future of AI in educational assessment3. 5. 1 Projections and potential developmentsLooking forward, AI in educational assessment is predicted to endure considerable breakthroughs. Chiu thinks that future AI systems would not only grade more efficiently but also deliver more detailed and insightful feedback to people [16]. The combination of AI with other developing technologies, such as augmented reality and virtual reality, could further increase interactive learning and tailored education. Advertisement 4. Enhancing feedback with AI4. 1 Personalized feedback through AI4. 1. 1 Customized responses and their impactThe integration of AI in education has ushered in a new era of individualized feedback, significantly altering student interaction with learning materials. Igbokwe studies AI systems that assess individual student replies, indicating distinct learning patterns and opportunities for improvement [17]. This individualized method enables feedback to be adapted to each student’s distinct learning style and pace, resulting in more effective support for specific educational needs. The impact of this personalization is enormous, leading to higher student performance, enhanced retention of knowledge, and a deeper comprehension of the subject matter. 4. 2 Immediate feedback and its advantages4. 2. 1 Benefits of real-time feedbackOne of the most transformational elements of AI in education is the provision of immediate feedback. Al Ka’bi addresses the advantages of real-time replies in educational environments [18]. Immediate feedback allows students to promptly rectify mistakes and reinforce accurate concepts, encouraging a more efficient and continuous learning process. This immediacy is especially advantageous in areas requiring a strong basic grasp, as it minimizes the building of misconceptions and bolsters confidence in learning. 4. 3 AI-driven feedback and student engagement4. 3. 1 Impact on student motivation and participationAI-driven feedback dramatically influences student engagement and motivation. George studies how AI systems, by offering timely and meaningful feedback, might dramatically enhance student participation in the learning process [19]. This greater engagement is linked to the interactive aspect of AI feedback, which tends to be more immediate and tailored compared to older techniques. [SEP] Justify:

Output: The text contrasts the claim by emphasizing that AI tools in education are increasingly focused on providing personalized feedback and enhancing student engagement rather than solely on automating administrative tasks like grading and attendance. It discusses how AI can deliver detailed feedback tailored to individual learning styles and promote interactive learning experiences, indicating a shift towards a more personalized educational approach instead of merely automating existing processes.


Input: Task: pass the claim using the given text. Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: The third and final stage in the development of physical dependence is withdrawal-induced ‘needing’. The needing stage is characterized by a desire to smoke that is so intense and urgent that it cannot be ignored and the individual is so distracted that he or she is unable to function normally [10]. In the words of one teen, “You really want one. You know you need it. You know you’ll feel normal after smoking, and you have to smoke to feel normal again” [10]. When smokers report that they need to smoke, it is not a need to experience pleasure, it is an acute and urgent need to relieve withdrawal symptoms [10]. This description of the Wanting-Craving-Needing stages is not based on, or predicted by any specific theory of addiction, it is a clinical description based on case histories, validated by larger surveys [9–12]. To the author’s knowledge, stages in the development of physical dependence on other drugs have not been identified. Traditionally, it had been assumed that long-term heavy daily use of tobacco was a prerequisite to dependence [13], and the DSM has suggested that prolonged heavy use was a prerequisite for withdrawal syndromes generally [14]. However, in a national survey, 16 % of adolescents who used tobacco only one or two days per month reported strong cravings to use tobacco, and this symptom of physical dependence increased proportionate to the frequency of tobacco use to 78 % among those who had initiated daily use [15]. Irritability and restlessness during withdrawal were reported by 13 % of adolescents that used tobacco 1–2 days/month and by 70 % of daily users [15]. Close to two dozen peer-reviewed studies document symptoms of physical tobacco dependence in nondaily or very light daily smokers [16]. Symptoms can appear after only a few uses of tobacco. [17] Studies demonstrate that physical withdrawal symptoms can be elicited by the administration of an opioid receptor antagonist after a brief exposure to morphine [18, 19]. These studies raise the possibility that clinically significant physical dependence on other drugs is present in some form soon after the onset of infrequent use. If so, we should not expect their symptoms to be identical to those seen during withdrawal among individuals with far advanced dependence. It is likely that only a mild ‘wanting’ to use the drug manifesting at a predictable interval after the last use might be the first sign of physical dependence. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the stages of physical dependence on tobacco and the symptoms related to withdrawal, with no mention of folklore or insights into cultural perceptions of nature.


Input: Task: corroborate the claim using the given text. Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: Proteins in reciprocal best hit files were matched to LPI numbers from DarkHorse for both the bacteria and archaea. If the LPI number was below 0. 2 for either the bacteria or the archaea, the reciprocal best hit was tagged as horizontally transferred (Table S4). For each archaeon, each bacterium with a horizontally transferred gene count more than 1. 96 standard deviations from the mean was defined as significantly enriched in horizontally transferred genes and considered to form an enriched pair with that archaeon. This technique is susceptible to multiple testing issues, but enrichment with three or more archaea is considered robust. Three bacteria were >1. 96 standard deviations from the mean for only one archaea and were not substantiated by enrichment in shared genes (above). These three bacteria were removed from further analysis as potential false positives. COlinking proteins to COGs or Clusters of were downloaded from NCBI ftp site. The COG collection used was originally created in 2003 (Tatusov et al. , 2003) and then used to identify COGs in subsequently sequenced genomes. The numbers of RBH in each category can be seen in Table S5. Fifteen categories had enough shared genes in them to examine statistically. COG categories were examined both with and without removal of the genome size effect. analyses, a four-parameter log–logistic function was fit between bacteria and each archaea for each COG category to remove the effect of genome size, as described above. The removal of the correlation of RBH and genome size was checked for each COG and archaea and was successful in all cases (R2 < 0. 001). Additionally, to further take GC into account, the effect of genome size and GC were simultaneously removed from the residuals for each archaea by multiple linear regression using the number of genes in the bacterial genome, reciprocal BLAST hits, and GC content. (Clarke, 1993) analyses were done with the Primer 6 program ( Results and the 448 bacterial genomes used in this study, 273 are from non-pathogens, 81 were anaerobes, 42 were thermophilic or hyperthermophilic (optimum growth temperature >50 °C; abbreviated as “(hyper)thermophilic”) (Fig. 1; Table S1). Regarding salinity, there were 72 moderately halophilic bacteria and 6 extremely halophilic bacteria (Table S1). [SEP] Justify:

Output: The text corroborates the claim by discussing methods used to identify horizontally transferred genes in bacteria. It specifically mentions that bacteria with a significantly high count of horizontally transferred genes form enriched pairs with archaea, indicating that horizontal gene transfer contributes to the acquisition of new traits in bacteria. The analysis and results presented suggest active gene transfer between bacteria and archaea, supporting the concept of horizontal gene transfer enhancing bacterial traits.


Input: Task: contrast the claim using the given text. Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: Such participation not only enhances motivation but also produces a more dynamic and participative learning environment, enabling students to actively engage in their education. 4. 4 Impact of AI feedback on learning outcomes4. 4. 1 Educational benefits of AI feedbackThe effect of AI-generated feedback on learning outcomes is significant. Chen presents many studies indicating how AI feedback can increase academic achievement and expand subject matter understanding [20]. This efficacy is largely owing to AI systems’ capacity to deliver precise, specific feedback addressing the complexities of a student’s responses. Furthermore, AI feedback generally includes ideas for development and extra learning resources, making it a full educational tool. 4. 5 Challenges and ethical considerations4. 5. 1 Addressing potential hurdles in AI feedback systemsDespite its many advantages, the deployment of AI in feedback systems offers problems and ethical considerations. Chen covers numerous topics such as potential biases in AI algorithms, the importance of data protection, and the need for transparency in system operations [20]. These problems underline the need for careful design, continual monitoring, and regular upgrades to guarantee AI feedback systems are egalitarian, fair, and respectful of student privacy. Additionally, they underline the significance of human oversight to complement AI feedback, ensuring the educational process stays sympathetic and student-focused. Advertisement 5. Efficiency and scalability in AI-enabled grading5. 1 Enhanced resource management in education5. 1. 1 The role of AI in optimizing educational resourcesEffective resource management is vital in education, directly impacting the quality and efficacy of teaching and learning. AI technology plays a vital role in this field. Alqahtani analyzes the impact of AI on grading systems, stressing not only the rapidity of the grading process but also a considerable improvement in the efficiency of resource allocation [21]. AI-enabled systems can efficiently process massive volumes of student work, a task that normally demands significant time and effort from educators. This quick processing ability frees up significant time for teachers, allowing them to focus more on direct educational activities such as lesson planning, individual student interactions, and pedagogical research. Furthermore, these tools assist detect specific areas where students require additional support, enabling teachers to adapt their education and interventions with greater precision. Beyond the classroom, AI increasingly influences resource management at the administrative level. Schools and educational institutions can gain large cost savings by decreasing the need for extra grading staff or outsourced grading services. [SEP] Justify:

Output: The text contrasts the claim by emphasizing that AI tools not only automate grading but also significantly enhance learning outcomes through AI-generated feedback. It discusses how AI feedback can increase academic achievement and expand understanding, rather than solely focusing on administrative tasks. Furthermore, it highlights AI's role in optimizing educational resources, suggesting a broader application of AI in educational contexts beyond mere administrative functions.


Input: Task: pass the claim using the given text. Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: Wanting has not been previously recognized as a withdrawal symptom, probably because of the perceived difficulty in distinguishing withdrawal-induced wanting from the wanting that we all experience as a part of daily living. The key to recognizing withdrawal-induced wanting is to apply the following criteria. Does the wanting recur predictably upon each withdrawal from the drug? Does it appear after a characteristic latency (time interval)? Is it relieved immediately upon using the drug? Is it followed by other symptoms in a predictable sequence? As physical dependence develops in new smokers, the symptoms of wanting, craving and needing develop in that order. During each episode of withdrawal, symptoms emerge in this same order: wanting, then craving, then needing. For individuals in the first stage of physical dependence, the desire to smoke never proceeds beyond wanting. For those in the second stage, the desire to smoke never proceeds beyond craving, while for those in the third stage, the desire to smoke will always progress to needing if abstinence is maintained. Thus, as physical dependence develops, it progresses through stages of wanting, craving and needing, and during each episode of withdrawal, symptoms escalate from a wanting phase, to a craving phase, to a needing phase. Table 1 presents a validated measure of the stages of physical dependence [20]. Individuals answer the three questions and the stage or level of physical dependence is determined by the most advanced symptom endorsed. Table 1 A clinical measure of the Stages of Physical DependenceFull size tableThe classically recognized symptoms of nicotine withdrawal such as irritability, anxiety, restlessness, moodiness, impatience, difficulty concentrating and trouble sleeping [7, 8], do not appear in a standard order as physical dependence develops but are usually associated with the needing stage of physical dependence and the needing phase of withdrawal. In practical terms, this indicates that the seminal work on tobacco withdrawal focused on individuals who had already advanced to the needing stage. Earlier stages of physical dependence were not discovered for many decades because light smokers had been systematically excluded as research subjects. Seminal studies on other withdrawal syndromes have similarly focused on treatment seeking individuals with advanced dependence [2, 3]. This raises the question as to whether similar clinical stages of wanting-craving-needing in the development of physical dependence to other drugs have gone unnoticed because of the natural tendency to focus on treatment seeking individuals with the most advanced dependence. Table 2 lists symptoms selected by the DSM as criteria for a tobacco withdrawal syndrome [7]. [SEP] Justify:

Output: The text is irrelevant to the claim because it discusses symptoms of tobacco withdrawal and the stages of physical dependence, with no mention or relation to folklore or cultural perceptions of nature.


Input: Task: corroborate the claim using the given text. Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: The outer ring represents the frequency of archaea-bacteria pairs enriched in reciprocal best hits (RBHs), where green represents 0–18 pairs, orange 19–25 pairs, and red 25–31 pairs. Download full-size image DOI: 10. 7717/peerj. 3865/fig-2 Figure 3: Genome size and number of reciprocal best hits fit using non-linear regression. Examples of (A) hyperthermophilic, (B) anaerobic, and (C) extremely halophilic archaea. The number of shared genes between archaea and bacteria were fitted using non-linear regression (black lines) and 1. 96 standard deviations from than the mean were calculated for the residuals (grey lines) to visualize the range. Bacteria-archaea pairs with reciprocal best hit (RBH) counts more than 1. 96 standard deviations greater than the mean are defined as enriched in RBHs. Green marker indicate bacterial endosymbionts where their oxygen utilization was unclear. axis is identical in all three graphs and labels are found in part (C). Download full-size image DOI: 10. 7717/peerj. 3865/fig-3 Eliminating the genome size effect Genome size can confound the quantification of HGT because the number of genes an organism accumulates is an adaptive strategy influenced by many other factors such as nutrient regime. A small genome allows a microbe to use fewer nutrients and to have a favorable surface to volume ratio, so microbes adapted to smaller genomes expel unused genes (Giovannoni et al. , 2005). A large genome in which excess genes are retained longer allows a microbe greater versatility (Chang et al. , 2011; Guieysse & Wuertz, 2012). Merely counting numbers of transferred genes does not take into account these lifestyle differences. In this paper we endeavored to determine which bacteria have engaged in more HGT with archaea than predicted by their genome size. A nonlinear genome size effect has been observed for pairwise comparisons of shared genes between bacteria and archaea (Snel, Bork & Huynen, 1999; Fuchsman & Rocap, 2006). The genome size of the bacteria is a much more important factor than the genome size of the archaea in the nonlinear genome size effect (Fuchsman & Rocap, 2006). [SEP] Justify:

Output: The text discusses horizontal gene transfer (HGT) between bacteria and archaea, highlighting how bacteria can acquire genes through this process, which supports the claim that HGT allows bacteria to acquire new traits. It mentions that the study endeavors to determine which bacteria have engaged in more HGT than predicted by their genome size, indicating that gene transfer is a means for bacteria to enhance their adaptability and versatility.


Input: Task: contrast the claim using the given text. Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: This efficient deployment of human and financial resources can be channeled toward strengthening many educational facets, including student support services, technological improvements, and professional development opportunities for educators. 5. 2 AI’s role in expanding educational horizons5. 2. 1 Adapting AI systems for large classes and online learningThe scalability and adaptability of AI systems are especially significant in big classes and online education, where the student-to-teacher ratio might be disproportionately high. Bauer highlights the inherent obstacles in these situations, such as ensuring equitable and uniform evaluation for a diverse and extensive student body and how AI effectively mitigates these issues [22]. In big classroom environments, traditional assessment procedures typically become unworkable due to the excessive volume of assignments needing examination. AI systems, unencumbered by the limits imposed by human graders, may adeptly manage thousands of assignments concurrently, assuring rapid and uniform grading. This scalability is critical for sustaining academic standards and insuring that students receive fast feedback, essential for their academic growth and progression. In online education, AI systems demonstrate extraordinary versatility. They can adapt to numerous course styles, academic fields, and diverse levels of student academic readiness. This flexibility is crucial in online platforms that usually serve an international and culturally heterogeneous student community. AI grading systems can interpret and evaluate responses across different languages and dialects and can be adjusted to adapt to specific curriculum standards and cultural situations. Moreover, in online education environments, AI systems can offer a level of customization that is impossible to attain in traditional classrooms. By evaluating individual student data, these systems can provide individualized feedback and learning ideas, enhancing the educational experience for each learner. In summary, the efficiency and scalability of AI-enabled grading systems constitute a significant leap in educational technology. They provide solid solutions for managing resources in education, successfully addressing the particular issues provided by big classroom environments and online educational platforms. As these systems improve, they hold the potential to significantly revolutionize the delivery and assessment of education, increasing learning experiences worldwide. Advertisement 6. Beyond traditional testing: AI in creative and subjective assessment6. 1 Evaluating creative work through AI6. 1. 1 Advanced techniques for assessing creativityThe application of AI in grading has predominantly been linked to objective assessments with clear right-or-wrong answers. However, recent advancements have extended AI’s capabilities to include the evaluation of creative work. [SEP] Justify:

Output: The text contrasts with the claim by providing a broad perspective on the role of AI in education beyond just automating grading and attendance. It emphasizes that AI can enhance various educational facets such as student support services, technological improvements, and professional development. Specifically, it discusses how AI adapts to large classes and online learning environments, highlighting that AI's capabilities extend to individualized feedback and creative assessment, which indicates its application is not limited to administrative tasks.


Input: Task: pass the claim using the given text. Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: As this list is limited to symptoms that would be experienced only by individuals at the needing stage, the DSM criteria are not a sensitive indicator of physical dependence. Table 2 DSM-5 tobacco withdrawal criteria [7]Full size tableThe stage of physical dependence, as measured by the instrument in Table 1, correlates with progressive changes in the neural architecture of the anterior cingulate gyrus [21, 22]. As the stage of physical dependence advances, neural pathways between the anterior cingulate gyrus and the precuneus increase in number, while those linking to the frontal lobe decrease substantially. Neural activity in networks involving the anterior cingulate increases in proportion to the strength of craving reported by individuals in withdrawal [22, 23]. Although the symptoms of tobacco withdrawal are primarily psychological, the correlation of both the stage of physical dependence and the intensity of withdrawal-induced craving with measures of brain structure and function confirm that physical processes are involved in the development and expression of physical dependence on tobacco. [21, 22] Changes in neural architecture have also been identified in conjunction with other forms of addiction [24, 25]. The progressive changes in neural architecture in parallel with stages of symptom development suggest that some of these changes represent neural adaptation rather than non-specific toxicity [26]. The identification of these changes among very light smokers suggests that it would be worthwhile looking for neural changes among users of other drugs who do not show the classic signs of physical dependence. As with the clinical research, imaging research has missed the opportunity to identify the progression in changes because studies have focused on individuals with advanced dependence. The latency to withdrawalThe latency to withdrawal is a measure of the time elapsed between the last use of tobacco and the onset of withdrawal symptoms. [10, 27, 28] As the symptoms of wanting, craving and needing emerge in that sequence during withdrawal, there is a latency to wanting, a latency to craving, and a latency to needing. The latencies vary between individuals, ranging from 4 weeks to as short as several minutes [27–29]. An 18 year-old woman reported wanting a cigarette after 30 min but being able to go two days before she absolutely needed one, while a 19 year-old man would want a cigarette in 2–3 h, and need one in 6–7 h [10]. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses entirely on the symptoms of tobacco withdrawal, neural architecture changes, and physical dependence on tobacco. It does not provide any information or insights related to folklore or cultural perceptions of nature.


Input: Task: corroborate the claim using the given text. Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: After determining shared genes from reciprocal best BLAST hits (Table S2), we explored two ways to predict the expected number of shared genes for each bacterial genome size: (1) the Korbel weighted average that utilizes both archaeal and bacterial genome size and (2) an empirical log–logistic function. While the fits were similar for bacterial genome sizes <5,000 bp, the empirical log–logistic function was superior for genomes >5,000 bp (Fig. S2) so was used for further calculations (Fig. 3, Fig. S1). Here, we denote archaea-bacteria pairs that have significantly more (>2 standard deviations from the mean calculated with the log–logistic function) reciprocal best hits (RBHs) than expected based on genome size to be enriched in shared genes (Table S3). This technique is particularly useful for identifying unusually large numbers of shared genes in bacteria with small genomes. Identifying horizontal gene transfer Genes can be shared between archaea and bacteria either due to horizontal gene transfer or due to vertical inheritance from their common ancestor. To test for the extent to which vertical inheritance can explain the numbers of shared genes between archaea and bacteria, we used a Mantel test to compare a similarity matrix derived from each bacterial genome’s number of shared genes with each archaeal genome to a similarity matrix derived from each bacterial genome’s 16S rRNA genetic distance to archaeal 16S rRNA. The correlation between the two matrices is significant (R = 0. 176 p < 0. 0001), but the R value is not high. This result implies that some, but not all, of the shared genes between archaea and bacteria were inherited from the last common ancestor, which is not surprising. To disentangle vertical inheritance and horizontal transfer, we used the DarkHorse algorithm, which searches for horizontally transferred genes using a probability-based, lineage-weighted method in which the lineage of each gene is compared to the lineage of the entire genome (Podell & Gaasterland, 2007). The phylogenetic distance between bacteria and archaea is very large, so we are only examining genes with extremely large lineage differences from the entire genome, which increases the robustness of our results. By cross-referencing our reciprocal best hit data with the DarkHorse results, we can flag some genes as potentially horizontally transferred (Table S4). [SEP] Justify:

Output: The text corroborates the claim by explaining that genes can be shared between archaea and bacteria due to horizontal gene transfer, as noted in the passage. The use of the DarkHorse algorithm to identify potentially horizontally transferred genes further supports the idea that horizontal gene transfer is a mechanism through which bacteria can acquire new traits.


Input: Task: contrast the claim using the given text. Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: This shift involves intricate processes wherein AI algorithms are trained to discern and assess elements such as creativity, originality, and artistic expression. Johnson and Lee have investigated the methods through which AI can appraise creative assignments [19]. These methods often utilize advanced machine learning techniques, including neural networks and deep learning, capable of analyzing patterns, styles, and techniques in various creative works. For instance, in evaluating written creative work, AI systems can analyze aspects like narrative structure, language use, and originality. In visual arts, they can examine color usage, composition, and artistic techniques. The subjective nature of creativity presents a unique challenge. Creative works, unlike objective tests, are open to various interpretations. Therefore, AI systems in this field aim to provide a structured evaluation, focusing on technical aspects and widely recognized standards of creativity while avoiding subjective judgment. This approach requires a delicate balance, ensuring that AI assessments both nurture and support creative expression, avoiding the imposition of overly rigid standards. 6. 2 AI’s flexibility in meeting diverse educational needs6. 2. 1 Tailoring learning to individual p7. Fairness and bias in AI grading7. 1 Tackling inherent biases in AI systems7. 1. 1 Identifying and reducing biasesThe challenge of inherent biases in AI grading systems is a key issue in educational technology. AI systems inherently reflect the biases existing in their data and algorithmic basis. Zhang investigates the ways biases might penetrate AI systems, such as through prejudiced training data, the subjective opinions of developers, or the simplifying of complicated human actions [24]. Identifying these biases involves a complete investigation of the AI system’s training data, algorithmic structure, and outputs. This process demands a multidisciplinary approach, incorporating not only technologists but also educators, sociologists, and students. Regular audits and updates are crucial to ensure in fairness and accuracy. Mitigating these prejudices is a continual undertaking. It needs broadening the training data to span a broad spectrum of student work across varied ethnicities, backgrounds, and learning styles. Additionally, adding feedback mechanisms where educators and students can review the AI system’s performance and fairness is crucial. This inclusive strategy assures the AI system is continually learning and changing, gradually decreasing biases. 7. 2 Establishing fair and inclusive grading practices7. 2. 1 Guidelines for equitable AI gradingCreating solutions for fair and inclusive AI grading is vital to guarantee these systems serve all students equitably. [SEP] Justify:

Output: The text contrasts the claim by detailing the use of AI in evaluating creative assignments rather than focusing on administrative tasks such as grading and attendance. It emphasizes AI's involvement in assessing elements like creativity, originality, and artistic expression, which suggests a broader application of AI tools in education beyond automation of administrative functions.


Input: Task: pass the claim using the given text. Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: The observation that latencies in some smokers are measured in weeks, while in others they are measured in minutes, reflects the fact that the latencies shorten with repeated exposures to nicotine [10, 29]. Since the short half-life of nicotine results in its elimination from the body within a day, it may seem paradoxical that withdrawal symptoms may not appear in novice smokers until several weeks have passed since their last cigarette. The mechanism that triggers tobacco withdrawal symptoms is unknown, but the idea that it is triggered by nicotine levels dropping below a threshold level is incompatible with the clinical evidence. Similar to the phenomenon whereby delirium tremens may not be experienced until several days after alcohol has been cleared from the blood [3], newly addicted smokers may not experience withdrawal symptoms until weeks after their last cigarette [10, 27–29]. In contrast, chain smokers report the need to smoke within minutes of having smoked [27, 28] while blood nicotine levels are still quite high [30]. Nicotine alters the expression of 162 genes in the brains of adolescent rats [31], triggers the release of a half-dozen neurotransmitters [32–35], and alters the production of neurotransmitters and neuronal responsivity for up to four weeks following a single dose [34, 36]. It is likely that mechanisms other than nicotine levels trigger withdrawal. Since nicotine withdrawal in humans is not triggered by the administration of an antagonist as is the case with opiate withdrawal, the mechanisms responsible for withdrawal symptoms may be drug-specific. Admittedly, this suggests that lessons learned about tobacco addiction may not apply to other drugs. On the other hand, non-addictive drugs can produce withdrawal symptoms, so some withdrawal symptoms may have little relevance to addiction, suggesting that differences in withdrawal mechanisms between drugs may not be relevant. Shortening latencies and the trajectory of tobacco useThe clinical implication of shortening latencies is that the length of time an individual remains comfortable after putting out a cigarette decreases over time. This increases the frequency at which cigarettes must be smoked to maintain comfort. [28] An increase in the frequency of smoking is one of the earliest signs of dependence [37]. At a latency to craving of two days, a person could keep withdrawal at bay by smoking one cigarette every other day. Fifteen cigarettes would be sufficient to keep withdrawal at bay for 30 days. [SEP] Justify:

Output: The text is irrelevant (pass) to the claim as it discusses the mechanisms of nicotine withdrawal and smoking behavior, which do not relate to folklore or cultural perceptions of nature.


Input: Task: corroborate the claim using the given text. Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: In general, the numbers of potentially horizontally transferred genes identified by DarkHorse are greater in bacteria that form archaea-bacteria pairs enriched in shared genes (Fig. 4 and Fig. S5). Over half (55%) of the bacteria in pairs with enriched RBHs also had greater than two standard deviations above the mean potential horizontally transferred genes identified by DarkHorse. Figure 4: Comparison of shared and transferred genes. Examples of HGT for anaerobic hyperthermophile Desulforococcus kamchatkaensis (A), anaerobic mesophile Methanosarcina acetivorans (B), aerobic mesophile Nitrosopumilus maritimus (C). In the first column the number of potentially horizontally transferred genes as determined from the DarkHorse algorithm is compared to the number of reciprocal best hits between bacteria and each archaeon. A positive value on the x-axis indicates the RBH count was greater than 1. 96 standard deviations from the mean. In the second column, the number of potentially transferred genes is compared to the number of those genes transferred from archaea to bacteria. In the third column, the number of potentially transferred genes is compared to the number of those genes transferred from bacteria to archaea. Color-coding describes whether the bacterium forms a pair with the archaeon listed (red), with any archaea in this study (blue), or not with any archaea (green). Download full-size image DOI: 10. 7717/peerj. 3865/fig-4 Correlations with growth conditions After the effect of genome size is removed, interesting trends emerge between the number of shared genes and the lifestyles of archaea and bacteria. For example, the enriched archaeal-bacterial pairs involving Desulfurococcus kamchatkensis, an anaerobic hyperthermophilic archaeon, predominately include (hyper)thermophilic bacteria (Fig. 3A). Similarly, Methanosarcina acetivorans, an anaerobic mesophilic archaeon, primarily forms enriched archaeal-bacterial pairs with anaerobic bacteria (Fig. 3B). Contrastingly, the number of archaea-bacteria pairs involving extremely halophilic bacteria and Haloarcula marismortui, an extremely halophilic aerobic mesophilic archaeon, are not significantly more than expected for their genome sizes (Fig. 3C). [SEP] Justify:

Output: The text corroborates the claim by discussing the identification of horizontally transferred genes in bacteria. It specifically mentions that 'the numbers of potentially horizontally transferred genes identified by DarkHorse are greater in bacteria that form archaea-bacteria pairs enriched in shared genes', which supports the idea that horizontal gene transfer enables bacteria to acquire new traits.


Input: Task: contrast the claim using the given text. Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: This integration is redefining how instructors and students interact with educational content and assessments. LMS platforms are rapidly adopting AI capabilities that provide real-time feedback on student assignments, suggest resources for improvement, and adapt learning content depending on individual student performance. This integration provides a more tailored and responsive learning experience, making education more flexible to individual learner needs. 9. 2 Preparing for advanced AI integration in education9. 2. 1 Infrastructure, ethical use, and trainingPreparing for the advanced integration of AI in education demands a diverse approach, focused on infrastructure development, ethical application, and extensive training. points out, securing the successful application of AI systems in education extends beyond just technological improvements [29]. Infrastructure development: Educational institutions must invest in the essential digital infrastructure to support advanced AI systems. This involves powerful internet connectivity, suitable hardware, and secure cloud storage solutions. Additionally, there needs to be a focus on designing interoperable systems that can combine diverse educational resources and platforms effortlessly. Ethical use: The ethical application of AI in education is crucial. Institutions need to develop explicit standards and policies that regulate the usage of AI systems. This includes guaranteeing data protection, eliminating potential biases in AI systems, and assuring openness in AI-driven judgments. Ethical use also requires ensuring that AI does not replace the key human parts of teaching but rather complements and enriches them. Training and professional development: Educators and administrative personnel need to be trained not only on how to use AI systems efficiently but also on understanding their limitations and potential biases. Continuous professional development programs should be created to keep educators current of the newest AI advances and best practices in integrating AI technologies into their teaching. The future of AI in grading appears optimistic, with technology growing more sophisticated and integrated into educational procedures. However, this future also involves careful planning and deliberation, ensuring that AI tools are used responsibly and efficiently to enhance the educational experience. Advertisement 10. Conclusion10. 1 Recap and future prospects10. 1. 1 Summarizing key themes and looking aheadAs we conclude our exploration of AI in grading, it is necessary to recall the key aspects and discuss future opportunities. This book has traveled the landscape of AI in education, from its earliest implementation in grading systems to the ethical, privacy, and technical difficulties it raises. We have seen how AI can boost the efficiency and fairness of grading, provide tailored feedback, and adapt to varied educational demands. [SEP] Justify:

Output: The text contrasts the claim by emphasizing that the integration of AI in education is not solely about automating tasks like grading, but rather about redefining how instructors and students interact with content and assessments. It highlights various applications of AI, such as providing real-time feedback, suggesting resources, and adapting learning content to individual needs, which extends beyond the scope of mere administrative automation.


Input: Task: pass the claim using the given text. Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: But when the latency to craving shortened to 45 min, one would have to smoke every 45 min to keep withdrawal at bay, which would entail smoking >500 cigarettes over a 30 day period. A 21 year-old woman described a latency-to-craving of two days after having smoked for about six weeks at age 16. Her latency decreased to four hours by age 16½, to two hours by age 17, to 1. 5 h by age 18, to one hour by age 19, and to 30–45 min by age 21. Over this time, her intake increased from 5 cigarettes/day to 15 [10]. Individuals addicted to opiates do not have to use their drug with such frequencies, but is it possible that at the onset of opiate dependence, a single dose may keep withdrawal symptoms at bay for many weeks, but a shortening of the latency to withdrawal demands a gradual escalation in the frequency of use? Aware of their latencies, smokers sometimes smoke in anticipation of a period of abstinence to postpone the onset of withdrawal (e. g. , prior to going to sleep, school or work). Even addicted smokers can smoke for pleasure, or to relieve stress or boredom. This is reflected in the fact that cigarette consumption correlates only moderately with the latencies (rho = −. 53, r = −. 53, and Kendall’s tau b = −. 53 in three studies of adolescents) [27–29]. This level of correlation indicates that only a proportion of cigarettes are smoked for the purpose of relieving withdrawal symptoms. Physical dependence need not be the only reason why addicted individuals self-administer their drug of choice, but it may put an outside limit on how long they can comfortably refrain from use. A new withdrawal period begins each time a smoker finishes a cigarette. From the moment the cigarette is finished the timer starts on the latency period during which withdrawal symptoms are not experienced. This asymptomatic phase of withdrawal may last from a few minutes to several weeks. The asymptomatic phase of withdrawal is followed by a symptomatic withdrawal phase during which wanting, craving, needing and the DSM withdrawal symptoms (anger, anxiety, restlessness, etc. ) emerge. The act of smoking aborts one episode of withdrawal and initiates another, analogous to hitting the snooze button on an alarm clock. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses addiction, withdrawal symptoms, and smoking behavior, without any reference to folklore or cultural perceptions of nature.


Input: Task: corroborate the claim using the given text. Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: In general, the bacteria that share unusually large numbers of genes with archaea (i. e. , after correction for genome size) are predominantly anaerobic and/or thermophilic (Fig. 5A). Bacteria isolated from hot springs, oil wells, sediments and activated sludge were over-represented in this set of bacteria (Fig. 5B). This result is remarkable considering that most of the bacterial genomes used in this study were from aerobic mesophiles (Fig. 5A). These trends are also true in the converse; i. e. , anaerobic and (hyper)thermophilic archaea are more likely to have large numbers of RBHs with bacteria (Fig. S6). In contrast, aerobic mesophilic archaea have a small number of RBHs with bacteria and form the fewest archaea-bacteria pairs enriched in RBHs (Fig. S6). Figure 5: Comparison of bacteria enriched in shared genes with archaea to the composition of the database. Pie charts comparing the bacterial database, excluding pathogens, to the bacteria that formed pairs enriched in shared genes (RBHs) with at least one archaeon. (A) Color coded with oxygen utilization and growth temperature of the bacteria (B) Color coded with the environment where the bacteria was isolated. Download full-size image DOI: 10. 7717/peerj. 3865/fig-5 To investigate more precisely which types of archaea and bacteria are more likely to share genes, we examined whether the archaea and bacteria in each enriched pair shared their growth conditions and habitats (Fig. 6). Indeed, in general, anaerobic bacteria share unusually large numbers of genes with anaerobic archaea, and (hyper)thermophilic bacteria share unusually large numbers of genes specifically with (hyper)thermophilic archaea (Fig. 6). One explanation of this result is that bacteria and archaea that live in similar environmental conditions are more likely to share genes with each other. However, this does not seem to apply to aerobic mesophilic bacteria. While all hyperthermophilic bacteria, 80% of thermophilic bacteria were enriched with at least one archaeal partner, and 56% of anaerobic bacteria were enriched in shared genes, Very few aerobic mesophilic bacteria were ever included in enriched pairs, even with aerobic mesophilic archaea (Fig. [SEP] Justify:

Output: The text corroborates the claim by discussing how bacteria share genes with archaea, particularly under specific environmental conditions. It highlights that bacteria and archaea living in similar environments are more likely to share genes, which directly relates to the concept of horizontal gene transfer, allowing these bacteria to acquire new traits from archaea.


Input: Task: contrast the claim using the given text. Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: The incorporation of advanced AI technologies like NLP and machine learning into educational procedures, particularly in grading, signifies a fundamental shift in how student achievement is judged. The opportunities of AI in this field are immense. As Pedró and Subosa imply, we are likely to see more advanced AI systems that not only evaluate with high accuracy but also provide nuanced feedback that helps lead students in their learning path [30]. Moreover, the incorporation of AI in grading is projected to expand in line with emerging educational technology. This includes the possible convergence of AI with augmented reality (AR) and virtual reality (VR) for more immersive learning experiences, as well as the usage of big data to get deeper insights into learning patterns and outcomes. However, as AI systems become more incorporated into educational settings, attention about ethical considerations and data protection becomes increasingly crucial. The future of AI in education will be influenced not just by technology breakthroughs but also by the laws and frameworks designed to guide its ethical and fair use. 10. 2 Embracing change and innovation10. 2. 1 The broader impact of AI in educationThe broader significance of AI in education extends beyond grading. and Kodikal emphasize, AI has the ability to alter the entire educational scene [31]. It can lead to more personalized and adaptable learning settings, where instruction is adapted to individual student requirements, talents, and learning styles. This customization could help bridge gaps in education, delivering support where it needs most and challenging people to realize their full potential. AI’s significance in education also necessitates a reevaluation of established teaching methods and curriculum frameworks. The data-driven insights offered by AI can guide more effective teaching practices and curriculum development, leading to a more responsive and dynamic educational system. However, embracing this transformation demands a coordinated effort from educators, lawmakers, technologists, and students. It includes not only accepting new technologies but also adapting to the cultural and educational shifts they bring. The role of educators, in particular, will shift, with a greater focus on guiding and mentoring students through a technology-enhanced learning journey. In conclusion, the future of AI in education is not just about technological innovation; it is about utilizing this technology to produce more equal, effective, and inspiring educational experiences. As we welcome these developments, it is vital to handle them intelligently, ensuring that AI is used as a force for good in defining the future of education. 10. 2. [SEP] Justify:

Output: The text contrasts the claim by highlighting that the role of AI in education extends beyond just grading and administrative tasks. It discusses the potential for AI to personalize learning, adapt teaching methods, and provide nuanced feedback, indicating a broader application of AI that goes beyond mere automation. The focus is on how AI can alter educational experiences rather than solely automating tasks like grading and attendance.


Input: Task: pass the claim using the given text. Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: Traditionally, when researchers referred to withdrawal they were referring to advanced withdrawal symptoms, as described in the DSM. However, under this new conceptualization of withdrawal, smokers are in a state of withdrawal (asymptomatic or symptomatic) anytime they are not actively smoking. In other words, the term ‘withdrawal’ is used to describe a physiologic state rather than specific symptoms. Analogously, when the cause of was unknown, symptomatic individuals were diagnosed with AIDS. After the underlying cause was better understood, individuals were described as carrying the human immunodeficiency virus, which is often an asymptomatic state. Smokers are in a state of withdrawal anytime they are not smoking. If they go too long without smoking they may experience withdrawal symptoms of increasing severity as time passes. Individuals with short latencies may experience symptomatic withdrawal dozens of times each day even though they are making no effort to maintain abstinence. Shortening latencies compel smoking at progressively shorter intervals and the resulting escalation in cigarette consumption can be mathematically modeled as a smooth trajectory [38–40]. The latencies shorten at different speeds and to different degrees in different individuals, determining whether that person will plateau as a light, moderate or heavy smoker. Longer latencies allow some physically dependent smokers to remain light smokers over a lifetime [41]. In others, the latencies shorten so much that they feel compelled to chain smoke. The fact that latencies shorten is the key to understanding the pathophysiology of tobacco dependence, its behavioral manifestations and its clinical course throughout the lifespan [42]. To the author’s knowledge, latencies have not been systematically studied in relation to any other drugs. Clinical manifestations of shortening latenciesThe trajectory of the frequency of tobacco use is very different from that of a drug like alcohol. When adolescents are drinking to achieve intoxication, the amount of alcohol consumed on weekends may increase substantially without a concomitant increase in the number of drinking days per week. With tobacco, the opposite pattern is seen. As the latency shortens, smoking frequency increases from monthly to weekly, to several days per week, and finally to daily smoking. All the while, the number of cigarettes smoked on smoking days remains around one to two [43]. Only when the latency shortens from days to hours is there a gradual increase in the number of cigarettes smoked per day [44]. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses withdrawal symptoms in smokers and the physiological aspects of tobacco dependence. There is no mention or connection to folklore or cultural perceptions of nature, making it unrelated to the claim.


Input: Task: corroborate the claim using the given text. Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: 001 Low vs significant pH (2 way w/Temp) Not significant with 16S Matrix 0. 176 0. 000 DOI: 10. 7717/peerj. 3865/table-1 Like the number of reciprocal best hits, the number of potentially transferred genes is highest among anaerobic or thermophilic bacteria and anaerobic or thermophilic archaea (Fig. 4 and Fig. S5). sp. BAV1, 12–31% of total shared genes were horizontally transferred, and this was fairly typical for bacteria enriched in shared genes. The numbers of transferred genes were larger than the residual from the upper confidence interval for shared genes, but these two numbers are not necessarily comparable. However, it does seem likely that a large proportion of the extra shared genes in enriched archaea-bacteria pairs (i. e. , those identified by our correction for genome size) were horizontally transferred. Figure 7: Examination of horizontal gene transfer in individual bacteria. Bacteria that form pairs with >3 archaea for either the number of residual reciprocal best hits (RBH) or potentially horizontally transferred genes, as determined by DarkHorse. The color coded bar graphs represent the number of archaea with which that bacterium forms enriched pairs (similar to Fig. 6). Bacterial names (left) are also color coded by bacterial oxygen utilization and growth temperature as in legend. Circles next to bacterial names indicate bacterial isolation environment. The column of numbers indicates the average number of horizontally transferred genes from archaea to bacteria for each category of archaea in the order aerobic thermophilic, anaerobic thermophilic, anaerobic mesophilic, and aerobic mesophilic archaea. The average number of HGT genes from archaea to bacteria for the entire dataset is four. Download full-size image DOI: 10. 7717/peerj. 3865/fig-7 The DarkHorse results on horizontal gene transfer can be evaluated to determine the direction of transfer (i. e. , from bacteria to archaea or from archaea to bacteria). Most documented cases of HGT between bacteria and archaea are predominantly unidirectional from bacteria to archaea (Wiezer & Merkl, 2005; Kanhere & Vingron, 2009; Nelson-Sathi et al. , 2015). [SEP] Justify:

Output: The text corroborates the claim by providing evidence of horizontal gene transfer (HGT) and its impact on bacteria. It mentions that a significant proportion of the shared genes among enriched bacteria had been horizontally transferred, indicating that horizontal gene transfer is responsible for bacteria acquiring new genetic traits. Additionally, it notes cases where HGT is documented, mainly illustrating the transfer of genes from archaea to bacteria, which further supports the notion that bacteria can acquire new traits through this mechanism.


Input: Task: contrast the claim using the given text. Claim: Natural language processing (NLP) is used in education to develop intelligent virtual assistants for student support. [SEP] Text: Max Silberztein2 233 Accesses AbstractNowadays, most Processing software applications use empirical “black box” methods associated with training corpora to analyze texts written in natural languages. To analyze a sequence of text, they look for similar sequences in a corpus, select among them the most similar one according to some statistical measurement or some neural-network-based optimization state, and then bring forth its analysis as the new sequence analysis. Here, I first show that the limited size of the corpora used and their questionable quality explain why most NLP applications produce unreliable results. Next, I examine the principles which are at the basis of corpus-based methods and uncover their linguistic naiveté. I finally dispute the scientific validity of empirical approaches. I propose solutions to various problems that are based on the use of carefully handcrafted linguistic methods and resources. This is a preview of subscription content, log in via an institution to check access. Access this 34. 9910 units per month Download 29. 95 Price excludes VAT (USA) Available as PDF Read on any device Instant download Own it forever Buy 119. 00 Price excludes VAT (USA) Available as EPUB and PDF Read on any device Instant download Own it forever Buy e159. 99 Price excludes VAT (USA) Durable hardcover edition Dispatched in 3 to 5 business days Free shipping worldwide - see info calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Notes1. Available at: See (Nelson & Kucera, 1979). 2. Available at: See (Davies 2009). 3. Available at: at: See (Taylor et al. 2003). 5. By wordforms, we mean contiguous sequences of alphabetic characters delimited by non-letters (a. k. a. delimiters). For instance, “Monday”, “soon” and “stepfather” are wordforms, whereas “week end”, “as soon as possible” and “father-in-law” contain respectively two, four and three wordforms. 6. [SEP] Justify:

Output: The text contrasts the claim by highlighting the limitations and unreliability of current NLP applications. It discusses how most processing software uses empirical methods that lead to unreliable results due to the quality and size of the corpora used. This suggests a skepticism towards the effectiveness of NLP, which is necessary for developing intelligent virtual assistants in education, thereby challenging the assertion that NLP is beneficial for student support.


Input: Task: pass the claim using the given text. Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: Individuals with very short latencies will begin to feel uncomfortable within minutes of finishing a cigarette, prompting chain smoking. Such individuals will spend a great deal of time smoking (Table 3, Diagnostic and 5 (DSM 5) criterion 3). Table 3 DSM 5 criteria [7]Full size tableAs the latency shortens, smokers may experience difficulty in refraining from smoking in situations where it is not allowed and may avoid such situations because the emergence of craving and needing makes them uncomfortable (Table 3, DSM 5 criterion 7). Individuals with latencies shorter than the time spent in bed may smoke just before going to bed and still feel the need to smoke immediately upon arising. The latency explains why the time to the first morning cigarette is a valid measure of dependence [15, 45]. Insomnia is a symptom of tobacco withdrawal and individuals with very short latencies may awaken during the middle of the night needing to smoke [46]. The discomfort of individuals in the needing stage of withdrawal may be such that they will smoke even when sick in bed because the discomfort of withdrawal makes them feel even worse, and why, even some nondaily smokers feel compelled to go outside in severe weather to smoke [37]. A note on medical and psychiatric approaches to diagnosisThe medical approach to identifying and diagnosing conditions is based on evidence of a disruption of normal anatomy or physiology [47]. The observation that the three stages of physical dependence (wanting, craving and needing) correlate with structural and functional changes in the brain establishes that physical dependence represents a disruption of normal anatomy and physiology [21]. As such, tobacco dependence can be diagnosed as a medical condition when symptoms of withdrawal-induced wanting, craving or needing are reported [47]. In practical terms, tobacco users fulfill the criteria for a medical diagnosis of tobacco dependence when they reach the wanting stage of physical dependence as indicated by endorsement of the first symptom on the instrument in Table 1. While medical conditions are diagnosed on the basis of indications of altered anatomy or physiology, the psychiatric approach to diagnosis is based on indications of impairment. Under the definition offered by the DSM 5, substance use represents a mental disorder when a combination of specified symptoms causes “clinically significant impairment or distress” [7]. Dissimilar to intoxicating and illegal drugs, tobacco use rarely causes incarceration, job loss or divorce. [SEP] Justify:

Output: The text is irrelevant to the claim because it discusses tobacco dependence and the symptoms associated with smoking rather than folklore or cultural perceptions of nature. There is no mention of folklore or insights into cultural perspectives in the provided text.


Input: Task: corroborate the claim using the given text. Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: The number of transferred genes from bacteria to archaea may be five-fold larger than the number of genes transferred from archaea to bacteria (Nelson-Sathi et al. , 2015). Our results agree with this trend for “normal” archaea-bacteria pairs (i. e. , those that are not enriched in shared genes); these pairs generally have more genes transferred from bacteria to archaea (Fig. 4 column 3; Table S4). However, of the 58 bacterial genomes that form pairs for enriched numbers of shared genes, over half had 11 or more genes horizontally transferred from archaea to bacteria (Fig. 7), whereas the mean number of archaea to bacteria transfers for all genomes is only 4 ± 7. Anaerobic and (hyper)thermophilic bacteria have more transferred genes from anaerobic and (hyper)thermophilic archaea, respectively, than from aerobic or mesophilic archaea (Fig. 7). Some bacteria, such as Dictyoglomus and Dehalococcoides, have 30–50 horizontally transferred genes from archaea (Fig. 7). These results suggest that the unusually large numbers of shared genes in our enriched archaea-bacteria pairs is primarily due to an unusually large number of gene transfers from archaea to bacteria in anaerobic and (hyper)thermophilic conditions. Examination of halophily Given that the numbers of shared genes are strongly associated with bacterial growth temperature and oxygen utilization, the lack of any significant correlation with halophily is surprising (Table 1) though consistent with results from Rhodes et al. (2011). There appears to be no difference in the numbers of shared genes between bacteria and archaea for non-halophilic and moderately halophilic bacteria. Of the six extremely halophilic bacteria, only two, Salinobacter ruber and Chromohalobacter salexigens, were ever enriched in RBHs. Salinibacter ruber, which is known to share genes with haloarchaea, including rhodopsins and a hypersalinity island (Mongodin et al. , 2005), was enriched in shared genes with each of the six extremely halophilic archaea. Salinibacter also possessed many horizontally transferred genes from archaea (Fig. 7). [SEP] Justify:

Output: The text corroborates the claim by discussing gene transfers from archaea to bacteria and highlighting that some bacteria have acquired numerous horizontally transferred genes (e.g., Dictyoglomus and Dehalococcoides have 30–50 horizontally transferred genes from archaea). This supports the idea that horizontal gene transfer is a mechanism through which bacteria can acquire new traits.