Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: 09, Figure 4A) supporting further the existence of only one complex. We discovered that the absolute area of the golden colouration increased with unpalatability (controlling for the effect of phylogeny and body size: GLS, F1,91 = 21. 1, p<0. 0001, Figure 4B) supporting the hypothesis that the golden colour is an aposematic signal. Figure 4 Download asset Open asset Effect of unpalatability. (A) Histogram of unpalatability of the mimetic complex with horizontal boxplots for each ring. (B) Relationship between the absolute area of dorsal golden colouration of mimics and their unpalatability. (C) Relationship between unpalatability and post-attack response of skinks to the six prey species from being eaten (0), to cleaning its mouth (0. 5) and spitting out the prey item (1). Predation pressure by a community of predators We quantified predation of mimics by the 12 most abundant predatory species that co-occurred with 13 sympatric mimetic species belonging to the complex (Table 2). We analysed gut content or faeces of these predators (553 spiders, 50 skinks, and 48 birds), representing three different guilds, using Sequencing, which allows identification of prey to species. We found that individuals of all predatory species were positive for primers targeting the DNA of ants, spiders, and mutillid wasps. The frequency of captured mimics was not significantly different from their availability in the environment (X212 = 1. 5, p=0. 99) because mimics were very rare in the field. Only three mimetic species (Camponotus aeneopilosus, Myrmarachne erythrocephala, Myrmarachne luctuosa) were detected in the faeces/gut of predators. Visually-oriented and non-visually oriented euryphagous (ant-adverse) predators captured mimics at less than 4% frequency, whereas specialized ant-eating predators captured mostly Batesian mimics (Table 2). The overall attack rate estimated for all predators was marginally significantly negatively related to the unpalatability of mimics when it included two Batesian mimics (GAM, F3. 8,4. 6 = 3. 8, p=0. 045, Figure 5A). [SEP] Justify:

Output: The text does not address the claim regarding the intelligence of ants. Instead, it discusses the ecological interactions between mimics and their predators, including data on unpalatability and predation pressure, without making any assertions about the intelligence of ants as a species.


Input: Task: corroborate 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: This customization seamlessly interfaced with the unique scheduling needs of each learner, offering flexibility that in traditional learning settings is typically constrained by external factors or intermediaries. Hana highlighted ChatGPT’s anytime, anywhere accessibility through a simple interface, enabling quick and comprehensive responses without the wait for expert assistance: ChatGPT is available to use anytime, anywhere using a simple and convenient interface. This would allow you to get a quick and comprehensive response at any time of the day, without having to wait around for people or experts to help you out. Lina similarly noted: It’s pretty great how available it is (as long as it’s not too busy…). Any question I have, I get an answer. It saved me a lot of Google searches and reading 379. Retrieved from June 2022. N. Public-private: unequal competition: Israeli public hospitals vs. the private healthcare system following government reforms. Organ Anal. 2021. L, Salvatore FP. Sustainability for healthcare organisations and systems: cultivating strategy and governance processes for a better future. In: editors. Business for sustainability. 2023; Volume I. 249. Palgrave studies in cross-disciplinary business research, in association with Euroof Business. Cham: Palgrave Macmillan. . Naamati-Schneider L, Zaks O. Public hospitals in crisis: managerial and strategic adaptation. In: editors. Business under crisis: volume II. Palgrave Macmillan; 2022. p. 43–64. A systematic literature review of artificial intelligence in the healthcare sector: benefits, challenges, methodologies, and functionalities. J Innov Knowl. 2023;8(1):100333. in drug information queries. J Telemed Telecare. 2023;2023:1–3. Artificial intelligence in healthcare. Eng. 2018;2(10):719–31. 301. p. 452–456. . . Artificial intelligence in health care: a report from the national academy of medicine. JAMA. 2020;323(6):509–10. Assessing the capability of ChatGPT in answering first-and second-order knowledge questions on microbiology as per competency-based medical education curriculum. Cureus. 2023;15(3):e36034. L, Sefton-Green J. The social utility of ‘data literacy. ’ Technol. [SEP] Justify:

Output: The text corroborates the claim by discussing how tools like ChatGPT provide customization and flexibility tailored to the scheduling and accessibility needs of learners. It highlights that ChatGPT is available 'anytime, anywhere,' allowing students to get quick and comprehensive responses to their questions, which emphasizes the adaptability of AI in educational contexts.


Input: Task: pass the claim using the given text. Claim: Epigenetics explores how certain environmental factors can influence gene expression across generations. [SEP] Text: Each library was sequenced using the v3-HS, in 76-bp paired-end mode on an Illumina HiSeq 2000 instrument following the manufacturer’s protocol. Images from the instrument were processed using the manufacturer’s software to generate FASTQ sequence files. RNA-seq analysisRNA-seq reads were aligned with the -seq pipeline v1. 7 ( which is based on the GEM mapper [73]. The pipeline aligns the reads in a sample in three phases, mapping against the reference genome (dicLab v1. 0c, Jul. 2012), against a reference transcriptome (track) and against a de novo transcriptome, generated from the input data to detect new junction sites. The sea bass genome used here is one of the best in silico annotated fish genomes [31]. After mapping, all alignments were filtered to increase the number of uniquely mapped reads. The filtering criteria included a minimum intron length of 20 bp, a maximum exon overlap of 5 bp and a filter step against a reference annotation checking for consistent pairs and junctions where both sites align to the same annotated gene. The libraries’ statistics including the number of raw reads and the average reads aligned can be found in Additional file 2. The same pipeline was used to quantify expression at the gene level. Similarity across RNA-seq samples was investigated with principal component analysis (PCA; Additional file 1: Fig. S8A), where the first principal component separated the samples by tissue type and explained almost 96% of the variance. One muscle sample was excluded from further analysis since it was clearly an outlier in the quality clustering. The variation in gene expression values was higher and with more extreme values in muscle (Levene’s test; p < 0. 001), whereas the testis had higher expression median (Mood’s median test; χ2 = 3372. 7, p < 0. 001; Additional file 1: Fig. S9A). Subsequently, the TMM method [74] was used for gene expression normalization, which takes into account not only library size (sequencing depth) of the samples but also the composition of the RNA population. The EdgeR robust method [75] was used for differential expression analysis. Genes with p adjusted < 0. 05 were considered significant. [SEP] Justify:

Output: The text is irrelevant to the claim, as it primarily describes a technical process related to RNA sequencing and gene expression analysis without mentioning epigenetics or the influence of environmental factors on gene expression across generations. Therefore, it does not provide any information that corroborates or contrasts the claim.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: Table 2 The percentage of predator individuals found with DNA (Sequencing) of mimics in their gut/faeces. The primer specific for ants amplified on average 9. 3% individuals of predators, the primer specific for Myrmarachne spiders amplified 3. 9% individuals of predators, and the primer specific for mutillids amplified 9. 2% individuals of predators. The predators (553 spiders, 50 skinks, 48 birds) were collected on the campus. They are grouped according to their guild. N gives the number of individual predators screened. The mimics are arranged from the most to the least palatable (left to right). Predatory guild Species (Family)P. ornata P. vermiculosa P. aurea P. ammon P. erato M. piliventris M. tepperi C. aeneopilosus E. aurata D. cephalotes D. nigricans M. erythrocephala M. luctuosa NVisually-oriented euryphagous Eulamprus quoyii (Scincidae)000000000000050Manorina melanocephala (Meliphagidae)00000000. 020000. 04048Sandalodes superbus (Salticidae)000000000000037Holoplatys planissima (Salticidae)000000000000012Ocrisiona sp. (Salticidae)000000000003. 83. 826Specialised ant-eating Servaea incana (Salticidae)0000000000001. 8114Euryopis umbilicata (Theridiidae)000000000009. 98. 6101Euryopis sp. (Theridiidae)000000000000012Hemicloea sp. 1 (Gnaphosidae)00000005. 00000020Hemicloea sp. 2 (Gnaphosidae)00000002. 40002. 42. 442Non-visually oriented euryphagous Lampona murina (Lamponidae)000000000002. 24. 346Clubionia robusta (Clubionidae)0000000000001. 758Clubiona sp. (Clubionidae)000000000002. 42. 485 Figure 5 Download asset Open asset Relationship between unpalatability and predation pressure. [SEP] Justify:

Output: The text is irrelevant to the claim about the intelligence of ants. It discusses predator individuals and DNA sequencing of various species, including ants, but does not provide any information regarding the intelligence of ants or any comparisons to other species.


Input: Task: corroborate 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: AbstractGenerative artificial intelligence (AI) can create sophisticated textual and multimodal content readily available to students. Writing intensive courses and disciplines that use writing as a major form of assessment are significantly impacted by advancements in generative AI, as the technology has the potential to revolutionize how students write and how they perceive writing as a fundamental literacy skill. However, educators are still at the beginning stage of understanding students’ integration of generative AI in their actual writing process. This study addresses the urgent need to uncover how students engage with ChatGPT throughout different components of their writing processes and their perceptions of the opportunities and challenges of generative AI. Adopting a phenomenological research design, the study explored the writing practices of six students, including both native and nonnative English speakers, in a first-year writing class at a higher education institution in the US. Thematic analysis of students’ written products, self-reflections, and interviews suggests that students utilized ChatGPT for brainstorming and organizing ideas as well as assisting with both global (e. g. , argument, structure, coherence) and local issues of writing (e. g. , syntax, diction, grammar), while they also had various ethical and practical concerns about the use of ChatGPT. The study brought to front two dilemmas encountered by students in their generative AI-assisted writing: (1) the challenging balance between incorporating AI to enhance writing and maintaining their authentic voice, and (2) the dilemma of weighing the potential loss of learning experiences against the emergence of new learning opportunities accompanying AI integration. These dilemmas highlight the need to rethink learning in an increasingly AI-mediated educational context, emphasizing the importance of fostering students’ critical AI literacy to promote their authorial voice and learning in AI-human collaboration. Similar content being viewed by others ChatGPT and the digitisation of writing 2. 1 AI-Assisted WritingResearchers have long been studying the utilization of AI technologies to support writing and language learning (Schulze, 2008). [SEP] Justify:

Output: The text corroborates the claim by discussing how generative AI technology, such as ChatGPT, is utilized by students to aid in their writing processes. It highlights that students engage with AI for brainstorming and organizing ideas, as well as addressing various aspects of writing, which indicates that AI is indeed being used to support personalized learning experiences. The mention of the need to rethink learning in an AI-mediated context further emphasizes the adaptation of educational practices to cater to student needs and performance.


Input: Task: pass the claim using the given text. Claim: Research is ongoing to understand the mechanisms of genetic memory in various animal species. [SEP] Text: Or the fact that exsanguination happens regardless of stunning. Or the fact that severing the major arteries of a conscious animal does not result in complete removal of blood from an animal, anyway. This paper does not even fixate upon asking whether a desire to consume animal tissue without the blood which accompanies it would justify causing avoidable suffering to the animals we slaughter. And by virtue only of where I was born, I do not happen to be a member of either the Jewish or Muslim faith, so I am not equipped to provide a fair argument on behalf of the practice of cutting conscious animals without any form of local or general anesthesia. I do not want to stake the claim that there is nothing holy about cutting an animal’s neck open. I do not want to stake the claim that, in every likelihood, the ancestors of these religious groups would have utilized firearms or captive bolt devices during their slaughter rituals, had the technology been available when these practices were established. texts tell that animals must be cut, the alternative to cutting at the time essentially boiled down to manually-driven blunt trauma. But since the religious forerunners did not stun animals, modern followers of these religions refuse to consider alternatives to the methodology which was likely the most appropriate available hundreds of years ago. These are all claims upon which I have devoted no time analysing. Claims upon which I will not fixate because they are within the realm of religious belief. I understand that I am in no position of authority to scrutinize anyone’s religious beliefs. Luckily, they are claims I do not have to go about supporting; I am tasked with operating in a secular consideration of the information at hand, both observational and scientific. This, in my mind, was sufficient in composing a compelling plea to lift the religious exemption to the HMSA. You, of course, are tasked with coming to your own conclusion. Final thoughtsThere are many topics I have no authority to confront. can do is comfortably claim that the animals we have domesticated for many generations and selectively breed into existence for our own purposes, should be animals for which we assume the highest degree of responsibility regarding wellbeing. We commandeer their reproduction, artificially inseminating females to constantly be pregnant and lactating. We select for those who put on weight the fastest (ex. Angus cattle). Or those who have the highest fat content (Wagyu) or produce the most milk (Holstein/Jerseys). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses issues related to animal slaughter, religious practices, and ethical considerations of domesticated animals, without any mention of genetic memory or related research. Thus, it does not provide any information supporting, contrasting, or addressing the claim.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: (A) Relationship between unpalatability and the total predation pressure (K-value) on 13 mimics by three guilds of predators (visually-oriented euryphagous, specialized ant-eating, non-visually-oriented ant-adverse). Estimated non-parametric regression model (GAM) is displayed. 2 for unpalatability of mimics. (B) Relationship between unpalatability of five mimics and total predation pressure (K-value) from three representatives of predators (see Table 3 for unpalatability of mimics). A high K-value indicates high predation while a low K-value indicates low predation. Solid symbols = Batesian mimics, hollow symbols = Müllerian mimics. Trials with selected predators We exposed five species of mimics (ants, spiders and bugs) of varying unpalatability (Table 3) and one non-mimic (spider) to one representative of each of three guilds of naïve predator species (euryphagous skink, ant-adverse spider, ant specialist spider). Eastern water skinks (N = 26) are visually-oriented generalist predators and captured (i. e. ate) non-mimetic spiders at significantly higher frequency than members of the mimetic complex such as ants, spiders and bugs (GEE-b, X25 = 125. 8, p<0. 0001, Figure 6). In accordance with our classification of unpalatability, we found a strong positive relationship between prey unpalatability and the post-attack response of skinks (GLM, F1,4 = 19. 4, p=0. 012, Figure 4C). Mimic or non-mimic spiders were immediately eaten, but skinks showed an adverse response to mimic ants and bugs. These prey were clearly distasteful because the skinks immediately cleaned their mouths and frequently spat the prey out. Table 3 A list of traits used to assess the unpalatability of five mimics. Values are means (±SE) estimated from 10 measurements. The species are arranged from the most to the least palatable. SpeciesFrequency of bitingSpray chemicalsNumber of spinesTotal spines length [mm]Cuticle thickness [mm]Total body size [mm]Mandible size [mm]Gland size [mm2]P. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses predation pressure and unpalatability among various species, including ants, without addressing their intelligence level. It focuses on ecological interactions and measurements of predation without any mention of cognitive abilities or comparative intelligence among species.


Input: Task: corroborate 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: Three major technological innovations have revolutionized writing: (1) word processors, which represented the first major shift from manual to digital writing, replacing traditional typewriters and manual editing processes; (2) the Internet, which introduced web-based platforms, largely promoting the communication and interactivity of writing; and (3) natural language processing (NLP) and artificial intelligence, bringing about tools capable of real-time feedback and content and thinking assistance (Kruse et al. , 2023). These technologies have changed writing from a traditionally manual and individual activity into a highly digital nature, radically transforming the writing processes, writers’ behaviors, and the teaching of writing. This evolution reflects a broader need towards a technologically sophisticated approach to writing instruction. AI technologies have been used in writing instruction in various ways, ranging from assisting in the writing process to evaluating written works. One prominent application is automatic written evaluation (AWE), which comprises two main elements: a scoring engine producing automatic scores and a feedback engine delivering automated written corrective feedback (AWCF) (Koltovskaia, 2020). Adopting NLP to analyze language features, diagnose errors, and evaluate essays, AWE was first implemented in high-stakes testing and later adopted in writing classrooms (Link et al. , 2022). Scholars have reported contrasting findings regarding the impact of AWE on student writing (Koltovskaia, 2020). Barrot (2023b) finds that tools offering AWCF, such as Grammarly, improves students’ overall writing accuracy and metalinguistic awareness, as AWCF allows students to engage with self-directed learning about writing via personalized feedback. Thus the system can contribute to classroom instruction by reducing the burden on teachers and aiding students in writing, revision, and self-learning (Almusharraf & Alotaibi, 2023). However, scholars have also raised concerns regarding its accuracy and its potential misrepresentation of the social nature of writing (Shi & Aryadoust, 2023). Another AI application that has been used to assist student writing is intelligent tutoring system (ITS). Research shows that ITS could enhance students’ vocabulary and grammar development, offer immediate sentence- and paragraph-level suggestions, and provide insights into students’ writing behaviors (Jeon, 2021; Pandarova et al. , 2019). [SEP] Justify:

Output: The text corroborates the claim by detailing how AI technologies, specifically through applications like Automatic Written Evaluation (AWE) and Intelligent Tutoring Systems (ITS), are utilized to aid students in their writing. The use of automated feedback from tools like Grammarly is highlighted, showing that these tools provide personalized feedback, which reflects the adaptability of AI systems to meet students' educational needs and enhance their learning experiences. By offering individualized support, these AI applications align with the claim that AI personalizes learning by adapting to student performance.


Input: Task: pass the claim using the given text. Claim: Research is ongoing to understand the mechanisms of genetic memory in various animal species. [SEP] Text: plantarum also shows 2. 3 fold increase approximately in longevity [296]. kdm5JmjC*, another kind of KDM5 mutant displayed impaired gut permeability, intestinal epithelium and microbiota. In this case as well, administration of L. plantarum rescued gut permeability, the defective social interaction and communication. These studies have helped establishing a link between gut microbiota and ASD. The probiotics that rescued ASD flies are good candidates for pharmaceutical companies to be sold as therapeutic. These results can be used for drug discovery and in treatment of these otherwise, untreatable disorders. 5. 3 Intestinal dysbiosis and infectionsDrosophila and its microbiome has provided invaluable information in understanding intestinal dysbiosis and chronic inflammatory infections. Two pathways that are present in Drosophila innate immune system to act against microbiota are immune deficiency (IMD) pathway (homologous to human NFκB pathway) and dual oxidase (DUOX) pathway. IMD pathway leads to anti-microbial peptide (AMP) synthesis whereas DUOX pathway leads to the formation of reactive oxygen species (ROS). Intestinal AMP overproduction changes the microbial community structure. This change is dysbiotic in nature because it leads to cell apoptosis of host cells [297]. IMD pathway over-activation leads to an increase in dysbiotic community structure and IMD inactivation leads to over-proliferation of community members in general [44]. Inflammatory bowel disorders also involve apoptosis of intestinal cells in humans which shows a link between the intestinal pathogenesis in both mammals and flies [297]. DUOX knockdown (DUOX-KD) flies show a high mortality rate after gut infections, which indicates DUOX-dependent ROS formation. These flies show huge amounts of microbial cells in the gut as opposed to wild-type flies. DUOX-KD flies survive normally in a GF environment. Inactivation of this pathway leads to host infections [298] whereas over-activation induces oxidative stress in host [299]. Extensive research in Drosophila midgut shows that intestinal epithelium damage induces inflammatory signals and growth factors to lead to ISC proliferation and tumor proliferation. 5. 4 Gut-cancer modelDrosophila is an important tool in medicine to explore the details of cancer. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses topics related to gut microbiota, intestinal disorders in Drosophila, and cancer models but does not mention genetic memory in any context. Therefore, it does not support or contrast the claim.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: ammon 0. 1143. 54 (0. 08) 0. 04 (0. 002) 9. 04 (0. 08) 1. 00 (0. 02) 2. 84 (0. 16) P. vermiculosa 0162. 96 (0. 08) 0. 03 (0. 002) 5. 98 (0. 05) 0. 71 (0. 03) 1. 62 (0. 06) C. aeneopilosus 0. 31000. 02 (0. 001) 8. 04 (0. 17) 0. 84 (0. 02) 2. 43 (0. 09) D. nigricans 00000. 02 (0. 0002) 7. 40 (0. 14) 00. 57 (0. 03) M. luctuosa 00000. 02 (0. 001) 7. 04 (0. 28) 00 Figure 6 Download asset Open asset Capture of mimics by three predators. Comparison of frequency of attacks on five mimics and one non-mimic by skinks (euryphagous visually-oriented predator); Lampona spiders (ant-adverse non-visually oriented predators), and Servaea spiders (specialised ant-eating predators). We found similar patterns in the non-visually oriented ant-adverse Lampona spiders (N = 25), which captured non-mimic spiders significantly more frequently than all mimics (Cochran test, X25 = 104, p<0. 0001, Figure 6), showing no differentiation between the mimics. By contrast, the visually oriented ant-eating specialist Servaea spiders (N = 27) readily attacked and captured mimics as well as non-mimics. Although these spiders readily attacked the mimics they did appear to differentiate between their levels of defence (attack: GEE-b, X24 = 15763, p<0. 0001, Figure 6). The most unpalatable ant was attacked at a significantly lower frequency than other mimics (contrasts, p<0. 008) and moderately unpalatable ant species were captured at a significantly lower frequency than bug and spider mimics (contrasts, p<0. 006). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses various predators' interactions with mimics and non-mimics without providing any information about the intelligence of ants or comparing them to other species. It does not support or contradict the claim regarding the intelligence of ants.


Input: Task: corroborate 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: Scholars also investigate chatbots as writing partners for scaffolding students’ argumentative writing (Guo et al. , 2022; Lin & Chang, 2020) and incorporating Google’s neural machine translation system in second language (L2) writing (Cancino & Panes, 2021; Tsai, 2019). Research suggests that adopting AI in literacy and language education has advantages such as supporting personalized learning experiences, providing differentiated and immediate feedback (Huang et al. , 2022; Bahari, 2021), and reducing students’ cognitive barriers (Gayed et al. , 2022). Researchers also note challenges such as the varied level of technological readiness among teachers and students as well as concerns regarding accuracy, biases, accountability, transparency, and ethics (e. g. , Kohnke et al. , 2023; Memarian & Doleck, 2023; Ranalli, 2021). 2. 2 AI into WritingWith sophisticated and multilingual language generation capabilities, the latest advancements of generative AI and large language models, such as ChatGPT, unlock new possibilities and challenges. Scholars have discussed how generative AI can be used in writing classrooms. Tseng and Warschauer (2023) point out that ChatGPT and AI-writing tools may rob language learners of essential learning experiences; however, if banning them, students will also lose essential opportunities to learn how to use AI in supporting their learning and their future work. They suggest that educators should not try to “beat” but rather “join” and “partner with” AI (p. 1). Barrot (2023a) and Su et al. (2023) both review ChatGPT’s benefits and challenges for writing, pointing out that ChatGPT can offer a wide range of context-specific writing assistance such as idea generation, outlining, content improvement, organization, editing, proofreading, and post-writing reflection. Similar to Tseng and Warschauer (2023), Barrot (2023a) is also concerned about students’ learning loss due to their use of generative AI in writing and their over-reliance on AI. Moreover, Su et al. (2023) specifically raise concerns about the issues of authorship and plagiarism, as well as ChatGPT’s shortcomings in logical reasoning and information accuracy. [SEP] Justify:

Output: The text corroborates the claim by discussing how adopting AI in literacy and language education can support personalized learning experiences, provide differentiated and immediate feedback, and reduce students' cognitive barriers. These aspects highlight AI's role in adapting to students’ needs and performance, thereby confirming the claim's assertion.


Input: Task: pass the claim using the given text. Claim: Animals inherit specific memories of individual experiences from their ancestors. [SEP] Text: , 2011) using read data of three previously studied isolates (Downing et al. , 2011) (BPK282/0cl4, BPK087/0cl11, BPK275/0cl18, sampled from the most frequent genetic populations ISC4, ISC5 and ISC6 respectively). We created artificial pairwise mixes of these samples in proportions of 1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2, 9:1 and unmixed, with depth levels adjusted for the relative proportions of unmapped reads in each. With these data, identified more rare variant SNPs in mixed libraries for the BPK275/0cl18-BPK087/0cl11 mix (Table A1. 2). BPK282/0cl4 was the reference genome strain and so should possess only heterozygous SNPs, and so its variant discovery rate was substantially lower when combined in mixed libraries. Consequently, for the mixed libraries of BPK282/0cl4-BPK087/0cl11 and BPK282/0cl4-BPK275/0cl18, once the fraction of non-reference reads <30%, the sensitivity of was superior to that for the GATK population caller. Table A1. 2 The fraction of discordant true and false SNPs between and GATK population-based variant calling. had a greater proportion of true SNPs – confirmed by visual inspection of mapping reads – both in terms of the number of variant sites, and across the population of samples. SNP caller1063399111876. 3%23. 7%89. 4%10. 6%GATK population caller3310611899123. 7%76. 3%10. 6%89. 4% Specifically, the population-based GATK and both called 11,389 variant genotypes in the artficially mixed samples, while GATK called 718 SNPs not detected using and found 396 additional SNPs not in the GATK set: a total of 1109 discordant variants at 139 sites. Visual inspection of mapped reads using Artemis (Carver et al. , 2008) and Samtools tview (Li et al. , 2009) indicated that 89% (354) of these 396 unique to were valid. [SEP] Justify:

Output: The text does not provide information related to the claim regarding the inheritance of memories in animals. It primarily discusses genetic variant calling and the performance of different methods in identifying SNPs (single nucleotide polymorphisms) in mixed genetic samples. Thus, it does not support, contrast, or relate to the claim in any discernible manner.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: In summary, euryphagous and ant-adverse predators were less likely to capture a mimic than a non-mimic while the ant-specialist attacked the ants and ant-mimics alike but the likelihood of attack varied according to the mimic’s actual unpalatability. There was no relationship between levels of unpalatability and the sum of predation pressure (K-values) exerted by the three predatory guilds (LM, F1,3 = 1. 83, p=0. 27, Figure 5B). have not been considered in Müllerian mimetic rings, which is perhaps surprising given their diversity and the number of known ant-mimics. The lack of ant fauna in mimetic rings possibly reflects an overall lack of conspicuous defensive coloration in ants. However, our study supports a species rich mimicry complex of golden coloured ant models and mimics, adding to the small number of documented large-scale complexes. Mimetic complexes (among several families of beetles) were first recognised by Nicholson (1927) for Australian insect fauna. Such complexes are presumably far more widespread than appreciated, but have only been subject to more intensive study relatively recently. Many more large-scale mimetic complexes among ants and other animals (particularly arthropods) are likely to be described in future. The species richness of this golden complex exceeds those of fish or millipedes (Marek and Bond, 2009; Alexandrou et al. , 2011) but is currently smaller than in mutillid wasp (Wilson et al. , 2012, 2015), heliconid butterfly (Mallet and Gilbert, 1995) and bumblebee (Williams, 2007) complexes. The key members in the golden complex are ants but the complex is taxonomically broad and includes species with distant phylogenetic origins. Beside the species mentioned, it may include a few other groups, such as bees, flies, and cerambycid beetles, in which some species also have a golden colouration. This is in contrast to complexes of similar scale that only include members from a single order. The distribution of the majority of the ten putative mimetic rings in the complex overlapped with at least one other ring. Although the cluster analysis distinguished rings, the distances between the rings were not large suggesting that the entire complex is just a single ring. [SEP] Justify:

Output: The text does not address the claim about the intelligence of ants. Instead, it discusses aspects of mimicry and predation involving ants and their mimics without making any claims or comparisons regarding intelligence. Therefore, it is irrelevant to the claim.


Input: Task: corroborate 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: Among the existing empirical research, studies have explored the quality of generative AI’s feedback on student essays in comparison to human feedback. Steiss et al. (2024) analyzed 400 feedback instances—half generated by human raters and half by ChatGPT—on the same essays. The findings showed that human raters provided higher-quality feedback in terms of clarity, accuracy, supportive tone, and emphasis on critical aspects for improvement. In contrast, AI feedback shone in delivering criteria-based evaluations. The study generated important implications for balancing the strengths and limitations of ChatGPT and human feedback for assessing student essays. Other research also examined the role of generative AI tools in L1 multimodal writing instruction (Tan et al. , 2024), L1 student writers’ perceptions of ChatGPT as writing partner and AI ethics in college composition classes (Vetter et al. , 2024), and the collaborative experience of writing instructors and students in integrating generative AI into writing (Bedington et al. , 2024). Specifically with regard to classroom-based research in L2 writing, Yan (2023) examined the use of ChatGPT through the design of a one-week L2 writing practicum at a Chinese university. Analyzing eight students’ classroom behaviors, learning logs, and interviews, the study showed that the use of generative AI helped L2 learners write with fewer grammatical errors and more lexical diversity. The study also found that the students’ biggest concerns were the threat to academic honesty and educational equity. This study is a pioneer in exploring students’ strategies and engagement with ChatGPT in writing; however, it was only conducted through a one-week practicum which did not involve authentic writing assignment tasks. Furthermore, students’ use of ChatGPT was limited to editing AI-generated texts instead of incorporating AI in a wider range of writing activities such as pre-writing and revising human generated texts. In another study by Han et al. (2023), the authors designed a platform that integrated ChatGPT to support L2 writers in improving writing quality in South Korea. Analyzing 213 students’ interaction data with the platform, survey results, as well as a focus group interview with six students and one instructor, the study found that the students generally held positive experiences with ChatGPT in supporting their academic writing. [SEP] Justify:

Output: The text corroborates the claim by detailing various studies that highlight the role of generative AI, such as ChatGPT, in supporting students in their writing processes. For instance, it mentions how generative AI tools are utilized in classroom settings, with research showing that the use of ChatGPT helped L2 learners reduce grammatical errors and improve their writing quality. Additionally, the study by Han et al. (2023) focuses on the positive experiences students had when using ChatGPT to enhance their academic writing, suggesting an adaptation to their specific needs in writing tasks.


Input: Task: pass the claim using the given text. Claim: Animals inherit specific memories of individual experiences from their ancestors. [SEP] Text: had a greater proportion of true SNPs both in terms of the number of variant sites, and across the population of samples. This illustrated ’s improved power for detecting variants at novel sites, compared with GATK (Table A1. 3). Table A1. 3 The rate of SNP calling for different combinations of read libraries of samples BPK282/0cl4, BPK087/0cl11 and BPK275/0cl18. BPK282/0cl4 was the reference genome strain and so should possess only heterozygous SNPs, and so its variant discovery rate was substantially lower. identified more rare variant SNPs in mixed libraries. Read library combinationsUnique to to GATK population callerBPK282/0cl4BPK275/0cl18BPK087/0cl1189%11%14140680%20%7182770%30%6191760%40%9193750%50%7194740%60%5196630%70%7191620%80%7178811%89%9145689%11%1050580%20%11112570%30%5129460%40%7131550%50%7131640%60%6132630%70%5133620%80%4130611%89%4127689%11%1564380%20%14107470%30%4129360%40%4132350%50%3133440%60%3132530%70%4131420%80%3130511%89%31266 discovered a total of 2418 SNPs (4. 3% of the original candidate set) in the 191 isolates from the core population. We calculated that our calling approach had a 99% chance of finding variants segregating at a frequency ≥0. 024 in the Core 191 and ≥0. 173 in ISC1 (Table A1. 4). Table A1. 4 The probabilities of observing alleles at a specific frequency in the core population and ISC1. We assessed whether or not the SNPs we discovered in our sample of ISC L. donovani were likely to be representative of the variation existing in the wider population of parasites circulating in the ISC. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses SNP (single nucleotide polymorphism) discovery rates and genetic variants in various samples without mentioning memories, inheritance of memories, or ancestral experiences. Therefore, it does not provide any information that supports or contrasts the assertion made in the claim.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: Our findings are not unlike those from other complexes that include rings of different colour pattern, which overlap in geographic distribution (Williams, 2007; Wilson et al. , 2012, 2015). An overlap of golden rings or actually their non-existence potentially suggests that the variable golden colour patterns produce similar aposematic signals. Large mimetic complexes include taxa of close and distant phylogenetic origins (e. g. , Linsley et al. , 1961; Rodriguez et al. , 2014) in which the colour pattern is not only a product of common ancestry but also of convergence. A shared ancestry is apparent for two species of Myrmarachne spiders (Pekár et al. , 2017), three species of Daerlac bugs (Casis and Symonds, 2012), and several species of Polyrhachis ants (Robson et al. , 2015). The majority though, including Camponotus, Diacamma, Pachycondyla, and Iridomyrmex ants, other spider species, Eurymela treehopers, and Ephutomorpha wasps, have converged on the same phenotype as evidenced by their isolation within lineages of non-mimetic colour patterns. The diversity and geographic pattern of the golden phenotype is likely a result of co-divergence as found in other complexes (e. g. , Rodriguez et al. , 2014). Species in Müllerian rings described to date, e. g. heliconid butterflies, use a single defensive trait to teach predators that they are distasteful (e. g. , Brower, 1958). The lesser-defended individuals in this context are less distasteful. Species in our golden complex use multiple defensive traits including a variety of structural and chemical defences. To understand how these traits interplay with the aposematic signal we created an index that summed all traits known to serve a defensive function. We acknowledge this approach is somewhat simplistic as various traits are differentially effective against various predators. For example, a thick cuticle is effective against arthropod predators but ineffective against vertebrates (Evans and Sanson, 2005). On the other hand, spines are effective against vertebrates but ineffective in the defence against arthropod predators (Mikolajewski et al. , 2006). Different species of closely related predators may also use a variety of adaptations to deal with the defences of prey. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses findings about color patterns, mimicry, and defensive traits within various species, including ants, but does not provide any information or evidence regarding the intelligence of ants or comparisons between species. Therefore, it does not support or contradict the claim.


Input: Task: corroborate 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: I chose to focus on first-year writing as the study setting, as it is a required course in most colleges and universities, thus a typical writing and learning context for most college students. First-year writing serves as the foundation for cultivating academic writing skills, with the aim of developing students’ essential literacy and writing proficiency needed for their undergraduate learning experiences. The 14-week course focused on English academic reading, writing, and critical thinking and consisted of three major units. This study focused on the last unit, which was about argumentative writing, a common type of academic essay writing (Association, 2020). The final essay asked students to form an argumentative response to a research question of their own choice. The unit, lasting for three weeks, was structured as follows (see Fig. 1): During the first week, the instructor spent two classes, each for 75 min, introducing ChatGPT (GPT 3. 5) as a large language model and inviting students to explore ChatGPT as a tool for writing. The instructor carefully chose and assigned five readings that allowed the students to grasp the ongoing academic and public debates and concerns regarding the use of ChatGPT in writing and educational settings. During the class sessions, students participated in various activities exploring the functionalities of ChatGPT, discussed ethics and academic integrity, and critiqued AI-generated writing. As part of the discussions on ethics, the instructor explicitly addressed academic integrity issues drawing upon both the writing program’s guidelines and the institution’s academic integrity policies to ensure that the students were aware of and committed to ethical use of generative AI in the writing class. During the second week, students learned various strategies for integrating sources in academic writing and practiced ways of using sources to build arguments. During the last week, students spent time peer reviewing each other’s work and met with the instructor individually to improve their drafts. Fig. 1Unit design with key topics and learning tasks over the three weeksFull size imageThe final essay allowed but did not mandate students to use ChatGPT. For those who used ChatGPT and AI writing tools, disclosure and transparency about how AI was used were required as part of the submission to the assignment. The instructor left using AI in their final essay as an open option to the students themselves, ensuring that students could pursue the option that works best for their individual needs. [SEP] Justify:

Output: The text corroborates the claim by describing a first-year writing course that incorporates the use of ChatGPT, a large language model, in a manner that is adaptive to student needs. The instructor introduced ChatGPT as a tool and encouraged students to explore its functionalities, allowing them to use it in a way that suited their individual academic requirements. The emphasis on ethical use and the option provided to students to employ AI reflects a personalized approach to learning.


Input: Task: pass the claim using the given text. Claim: Genetic memory can enable animals to learn complex tasks without ever observing them firsthand. [SEP] Text: It has tasked them with making 80 new pits per year by 2030, a deadline NNSA admits it will not meet. Los Alamos’s pits will be made at a facility called PF-4, a set of high-security buildings surrounded by cyclone fences with razor wire. Inside PF-4 are glovebox enclosures—radiation-shielded workstations where workers use thick gloves and peer through glass windows to manipulate the exotic metal. The lab is hiring thousands of workers, and its first pit is likely to be ready for the stockpile next year. To fashion plutonium into “pits” for bombs, the radioactive metal must be handled within protective glove boxes. gargantuan effort is motivated by a simple fact: many current pits are more than 40 years old, and plutonium behaves in confounding ways as it ages and radioactively decays. A green, fuzzy coating grows on it as its surface oxidizes. Atoms in its metallic lattice are knocked out of place as it spits out uranium isotopes. Its dimensions shift when it slips between six different solid phases. And the pits do not necessarily degrade smoothly. “We know at some point there will be a nonlinear piece,” says David Clark, director of Los Alamos’s and editor of the Plutonium Handbook. “We just haven’t seen it. ” So far, the silvery spheres seem to be holding up. Internal and external assessments have vouched for their integrity, suggesting the pits could have decades of viability left. “We haven’t seen any issues,” Clark says. But Jason, a secretive group of physicists who advise the government on national security matters, raised concerns that galvanized DOE. In a 2019 report, the group urged the agency to reestablish pit production “as expeditiously as possible” to “mitigate against potential risks posed by Pu aging. ” One might think the new pits would make it easier to certify the stockpile, by avoiding the uncertainties of aging plutonium. But they come with uncertainties of their own. The new pits won’t be twins of their predecessors, so weapons scientists will have to understand how the alterations change pit behavior. They are being manufactured using recycled and purified plutonium from old pits, not fresh material, unlike the originals. Moreover, they will be made with different processes, and in some cases designed to slightly different specifications. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the production of plutonium pits and issues associated with aging plutonium at a lab, with no mention of genetic memory or animal learning. Therefore, it does not corroborate or contrast with the claim.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: For example, some spiders are able to catch ants even with a thick cuticle while others are not (Pekár and Toft, 2015). A true index of unpalatability requires an estimation of the total effectiveness of a combination of defensive structures, such as spines and thick cuticle and the toxicity of chemical compounds for each predator-prey combination. Estimates including spines and cuticle thickness may be relatively straight forward but information on the toxicity of chemical compounds is largely not available (see Although our approach is simplistic, the positive relationship between the index of unpalatability and the response of skinks strongly suggests that the measure is a good proxy for unpalatability. Some of the defensive traits were strongly correlated with body size. Body size alone is an anatomical constraint that may prevent predation, thus it is difficult to separate the effect of pure body size from the relative size of some defensive traits (e. g. , spines). A strong correlation between body size and the size of defensive traits is natural, because larger spines, for example, require a larger body to support them. It is, however, the absolute size of defences not their relative size that is ecologically important (e. g. , a skink will swallow larger spines with greater difficulty than smaller ones). Whatever their single effect is, all these traits together, add to an overall unpalatability of mimics. Our study describes a new complex with a golden/black colour combination. This combination is somewhat similar to the classic aposematic yellow/black phenotype (Ruxton et al. , 2004; Williams, 2007). Accordingly, our findings support the idea the coverage of golden colour acts as an honest signal of aposematism. All the predators in staged trials exhibited an aversion to the prey with the greatest coverage of golden colour. Furthermore, golden individuals were rarely found in the diet of natural populations of predators. Honest signalling is further supported by the positive correlation between the extent of the golden colour and unpalatability, controlling for body size in the analyses via phylogenetic correlation or as a covariate. The theory of honest signalling, however, predicts such a relationship within rather than across species. However, recent evidence shows that this relationship can also exist across closely related aposematic species (Cortesi and Cheney, 2010; María Arenas et al. , 2015). [SEP] Justify:

Output: The text does not address the claim regarding the intelligence of ants. It focuses on predator-prey dynamics involving spiders and skinks, discussing various defensive traits and their relationships without mentioning ants or their intelligence. Therefore, it is irrelevant to the claim.


Input: Task: corroborate 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: Thus the unit provided various opportunities and flexibility for planning, researching, drafting, reviewing, and editing with ChatGPT throughout students’ writing process. There were 11 students, all freshmen, enrolled in the class. All but one reported using ChatGPT in their writing. Six students were recruited based on their willingness to participate and the diversity of their first language to ensure a balanced coverage. Table 1 shows the demographic information of the students (with pseudonyms). Table 1 Demographic information of student participantsFull size table4. 2 Data CollectionAligned with an interpretive phenomenological design that focuses on exploring participants’ lived experiences and how they construct meaning of their own experiences (Smith & Shinebourne, 2012), I collected three major types of data in order to uncover the students’ writing processes involving ChatGPT and their perceptions. First, I collected students’ written products and artifacts such as in-class writing, screenshots of students’ conversations with ChatGPT, informal short writing assignments, and the formal writing assignments for the final argumentative essay. Second, I collected students’ written self-reflections about their use of ChatGPT in writing. Finally, the participants were interviewed for around 30–40 min, and all interviews were audio-recorded. These semi-structured interviews were developed around students’ former experiences with ChatGPT, their views of the tool, and the ways they utilized ChatGPT for their writing assignments in this class. Students’ conversational screenshots with ChatGPT and their in-class and outside class writing drafts could demonstrate their interactions with AI as well as the changes they made upon contemplating the responses from the chatbot. The interviews and students’ self-reflections could further shed light on their perceptions and decision-making. Multiple sources of data helped to understand students’ behaviors, perceptions, and engagement with AI during different stages of writing. Triangulation of the data also helped me to understand students’ rationale for and practices of integrating, discounting, and reflecting on the chatbot’s output into their writing. It is important to note that a phenomenological qualitative research design like this aims to provide in-depth understanding and insights into participants’ experiences. The context of the study—a first year writing class—and the specific type of assignment investigated are both common scenarios in college classrooms, thereby enhancing the study’s relevance despite its limited sample size and scale. [SEP] Justify:

Output: The text supports the claim by illustrating how AI, specifically ChatGPT, is being utilized in a writing class to enhance students' learning experiences. It details the various ways students adapted their writing processes—planning, drafting, and revising—through interactions with ChatGPT. Additionally, the mention of collecting self-reflections and interviews indicates that the use of AI is tailored to each student's needs and perceptions, which aligns with personalizing learning experiences.


Input: Task: contrast the claim using the given text. Claim: Many personal AI assistants use natural language processing to understand and respond to human speech. [SEP] Text: The task of the interface is to channel the request you make to a service that deals only with requests of that particular kind, whether that be modifying your phone settings, finding movie times or making a restaurant booking. If it’s not the case that ‘there’s an app for that’, then the IPA falls back on a search engine query. Of course this is, in its own way, a great model. A divide-and-conquer strategy makes it possible to focus on providing functionalities that address high value or high frequency tasks, leaving the long tail of minority interests for later. And it makes it possible to spread the burden of development in a way that encourages innovation: with an appropriate API, third parties can add new functionalities to the base platform, providing an ever-growing range of capabilities. But at the end of the day, what you have is a collection of essentially distinct capabilities. While there’s no doubt that these capabilities can be extremely useful, the whole is no more than the sum of the parts. A collection of diverse domain-specific functionalities doesn’t add up to intelligence. In the medium term, there are two ways this might play out. On the one hand, the number of distinct functionalities supported by the platform might increase beyond what can sensibly be managed by this kind of architecture, perhaps resulting in the user frequently being routed to a functionality that wasn’t the one they were actually looking for: the more categories you have, the greater the risk that categorisation will drop you in the wrong bin. Or, we might acknowledge that there’s a threshold to the number of distinct capabilities that can be navigated via such an interface without frustration, in the same way as having multiple screens of app icons on your phone just makes it harder to find things, so that you end up removing apps to make navigation more manageable. Either way, the point remains: assembling a collection of specific capabilities doesn’t amount to a broad-coverage intelligence. Sorry, ScarJo’s Samantha isn’t coming to a phone near you any time soon. Of course, we’ve seen this phenomenon repeatedly in the history of AI. Early AI research explored what a general problem-solving machine would look like, but in practical terms it was domain-specific expert systems that actually demonstrated value. [SEP] Justify:

Output: The text contrasts the claim by arguing that a collection of domain-specific functionalities does not equate to genuine intelligence, implying that while personal AI assistants may handle specific tasks, they lack the comprehensive understanding and responsiveness that genuinely intelligent systems would exhibit. The discussion focuses on how these systems manage distinct capabilities rather than truly understanding natural language in a broad context.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: It remains to be tested what speciation process and selection forces have driven this pattern. Given the fact, that the complex is composed of many unrelated taxonomic groups that occur in different micro-habitats, the processes are likely to vary. It is necessary to investigate whether such a relationship between species holds for other known mimetic complexes. Unlike the classic yellow/black combination, members of the golden complex are defined by a metallic golden sheen. This sheen reflects brightly under direct sunlight making the animals very conspicuous, at least to a human observer. Just how important this sheen is in deterring a predator over and above just a matt colour has yet to be determined for this system. In other systems though, reflective iridescence has a significant additive effect (e. g. Fabricant et al. , 2014) so it is likely that the sheen enhances the detectability of the signal. A possible additional benefit, or even an alternative explanation, is that the sheen is important for reflecting harmful radiation and dissipating heat (Shi et al. , 2015). Irrespective of the explanation the importance of this colour variant, further experimental investigation is warranted. The aversion to prey with golden colour was apparent irrespective of the predator’s visual sensitivity. Visually- and non-visually orienting predators equally avoided the golden prey suggesting that golden mimics advertise their unpalatability via multiple sensory modalities. The non-visual predator still proportionally avoided the mimics despite not being able to detect the visual signal. This is an area of promising future investigation. Some non-visual predators potentially recognize mimics via the patterns of vibrations generated by their movements (Pekár et al. , 2011). Predators typically use multisensory modalities to identify prey (Bro-Jørgensen, 2010) such as movement, shape and defensive chemicals they emit. Our measure of unpalatability suggested that the highest proportion of individuals was moderately defended and defensiveness is distributed from highly defended to weakly defended. This is in agreement with previous claims that mimicry rings are dominated by moderately defended species (Sherratt, 2008). With a predominance of moderately defended prey, there is an expectation that their presence will erode the effectiveness of the model. Our findings, based on a limited number of mimics, multiple predatory guilds when taking into account, did not support this notion. [SEP] Justify:

Output: The text is irrelevant to the claim about ants being the most intelligent species on Earth. It discusses various aspects of mimetic complexes, predator-prey interactions, and the significance of coloration in terms of visibility and defensiveness, without mentioning anything regarding the intelligence of ants or any comparison to other species.


Input: Task: corroborate 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: In order to narrow down the topic and find a good focus, she used ChatGPT for exploring possible directions she could pursue. As she noted: It immediately gave me suggestions to approach the cause from demographic changes, economic factors, traditional gender roles, governmental policies, and cultural attitudes with detailed explanations beside each suggestion. So, I went on to pick economic reasons, which I think were the most accessible to write about. (Lydia, Reflection) ChatGPT’s feedback facilitated a smoother decision-making process for Lydia regarding the specific topic to further investigate. Another student Kevin mentioned that running his initial research idea into ChatGPT was helpful because ChatGPT gave him “some relevant ideas that hadn’t crossed his mind when thinking about the topic” (Kevin, Written Assignment). Considering ChatGPT’s suggestions did not mean that the students just took them for granted and incorporated them unquestioningly. For instance, Nora was interested in writing about the impact of AI on human lives. Upon putting her initial research question into ChatGPT, she found the feedback helpful and decided to do more research on the aspects highlighted by ChatGPT (see Fig. 2). Fig. 2Screenshot of Nora’s conversation with ChatGPTFull size imageStudents also reported using ChatGPT for outlining. Emma used ChatGPT extensively to help organize her outline and shared her procedure as follows: I wrote my own outline first consisting of my own ideas and then put it into ChatGPT. I asked ChatGPT to make the outline flow better. I was surprised with the results it gave me. It made the ideas more concise and connected better off of each other. . . I tried it a few times, and every time it gave me a different version of the outline that I could potentially use. I ultimately compared the information from my sources and chose an outline I thought best suited my essay and my essay question. (Emma, Reflection) Emma’s approach revolved around utilizing ChatGPT to unearth linkages among her various initial yet disorganized ideas she already had. By experimenting with diverse ways to build coherence and connection among her thoughts with the aid of AI, she shortcut the mental task of structuring her ideas from scratch. Using ChatGPT for refining the flow of ideas was also a strategy adopted by other students, but not always during the outlining stage. [SEP] Justify:

Output: The text corroborates the claim by highlighting how students utilize ChatGPT to adapt their research processes according to their personal needs. Students like Lydia, Kevin, and Emma experienced tailored suggestions and organization strategies, indicating that AI tools like ChatGPT effectively personalized their learning experiences by reflecting their individual inquiries and preferences.


Input: Task: contrast the claim using the given text. Claim: The majority of AI assistants rely on cloud computing to process complex commands and store data. [SEP] Text: 1049/PBPC054E_ch12 text PDF pandemic has forced industries to move immediately their critical workload to the cloud in order to ensure continuous functioning. As cloud computing expansions pace and organisations strive for methods to increase their network, agility and storage, edge computing has shown to be the best alternative. The healthcare business has a long history of collaborating with cutting-edge information technology, and the Internet of Things (IoT) is no exception. Researchers are still looking for substantial methods to collect, view, process, and analyse data that can signify a quantitative revolution in healthcare as devices become more convenient, and smaller data becomes larger. To provide real-time analytics, healthcare organisations frequently deploy cloud technology as the storage layer between system and insight. Edge computing, also known as fog computing, allows computers to perform important analyses without having to go through the time-consuming cloud storage process. For this form of processing, speed is key, and it may be crucial in constructing a healthcare IoT that is useful for patient interaction, inpatient treatment, population health management and remote monitoring. We present a thorough overview to highlight the most recent trends in fog computing activities related to the IoT in healthcare. Other perspectives on the edge computing domain are also offered, such as styles of application support, techniques and resources. Finally, necessity of edge computing in the era of Covid-19 pandemic is addressed. for medical images on the basis of intelligence and biocomputing M. Mohammed Mustafa, S. Umamaheswari, p. 279–302 (24) text PDF in medical imaging explores how intelligent computing can create a large amount of changes to existing technology in the field of medical image processing. The book presents various algorithms, techniques, and models for integrating medical image processing with artificial intelligence (AI) and biocomputing. Bioinformatics solutions lead to an effective method for processing the image data for the purpose of retrieving the information of interest and collecting various data sources for extracting the knowledge. Moreover, image processing methods and techniques help scientists and physicians in the medical field with diagnosis and therapies. It describes evolutionary optimization techniques, support vector machines (SVMs), fuzzy logic, a Bayesian probabilistic framework, a reinforcement learning-based multistage image segmentation algorithm, and a machine learning (ML) approach. It discusses how these techniques are used for image classification, image formation, image visualization, image analysis, image management, and image enhancement. [SEP] Justify:

Output: The text contrasts the claim by highlighting that while cloud computing is commonly used for real-time analytics and data storage, edge computing is presented as a significant alternative. It emphasizes the advantages of edge computing, particularly in healthcare settings, suggesting that it can perform important analyses without relying on the cloud, which challenges the notion that AI assistants primarily depend on cloud computing.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: Instead, all mimics investigated in our predator-prey trials experienced significantly lower mortality than the non-mimetic species, and this was irrespective of the coverage of gold. We found the skinks were more likely to attack the lesser-defended individuals but when the suite of predators was taken into account there was little variation in the overall predation pressure. This is potentially because some predators may not use the visual signal as a reference and are cuing in on other signals. Diet analyses of several potential predators also showed predators of all guilds, including non-visually oriented predators, captured mimics with very low frequency. The predators that we used are known to feed on non-mimetic prey too (Jackson and Harding, 1982; Daniels, 1987; Platnick, 2000; Higgins et al. , 2001; McGinley et al. , 2015). Indeed, they fed on prey related to the mimics as evidenced by the positive results of designed primers targeting the mimics. We did not measure the availability of alternative prey so we do not have an absolute indication of relative encounter rates. However, birds learn to avoid both Müllerian models and mimics irrespective of the availability of alternative prey (Lindström et al. , 2004). In addition, there are multiple species of ant and ant models distributed across the sampling habitat occupying both terrestrial and arboreal habitats. The majority of the species in the complex are also ants and ants are typically super abundant due to their sociality. So it seems likely the predators would see these animals on a regular basis. Using a multi-disciplinary approach here, we faced several constraints. For example, measurements of mimic colour were based on photographs as we were unable to collect all mimics. To correct for inherent differences in illumination used during photographing mimics, the obtained RGB values were standardized but not linearised, and thus should be interpreted with caution. Similarly, the results of experiments with skinks must be interpreted with caution due to constrained randomisation used (Müllerian mimics first). The response of skinks could be affected by the constrained prey order (due to some kind of habituation). Yet, other predators used showed similar response to mimics as skinks, though the order of offered prey was fully randomised. Here we present the first account of the golden mimetic complex, which we believe is an exciting model for future research. [SEP] Justify:

Output: The text does not address the claim regarding the intelligence of ants. Instead, it focuses on predator-prey interactions, mimicry in species, and the ecological context without making any reference to the intelligence of ants or their comparison to other species in terms of intelligence.


Input: Task: corroborate 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: For instance, after completing her first draft, Lydia “copied and pasted her entire essay into the chatbox and asked for suggestions on how to improve the structure and argument” (Lydia, Reflection). Lydia underlined that her revision process with ChatGPT was iterative, as she put her revised version back into the chatbot and went through another round of feedback and subsequent revision. Additional applications reported by students also encompassed employing ChatGPT to reduce redundancy and enhance conciseness of content (Emma) as well as to refine topic sentences for accurately summarizing the main ideas of body paragraphs (Kevin). Apart from utilizing ChatGPT to assist with global level issues such as structure, argument, and coherence, the students also harnessed the AI tool for sentence-level issues. They unanimously agreed that ChatGPT was a valuable tool for language editing. Alex, a L1 student, commented that ChatGPT could edit essays “exceptionally well. ” Alex not only used the AI tool to help improve the syntax of his writing such as “run-on sentences” but also consulted it as his dictionary for “providing academic diction” (Alex, Interview). The L2 participants particularly acknowledged ChatGPT as beneficial for enhancing the accuracy of their writing. Lydia shared that upon completing a paragraph of her essay, she would put it into ChatGPT and ask it to “revise the wording and grammar only” so she could refine her language and keep the content original (Lydia, Reflection). Another L2 student Nora noted that “when I struggle with expressing my thoughts accurately in English words, ChatGPT can help me express those ideas in a more powerful and accurate way. It removes communication barriers” (Nora, Written Assignment). 5. 2 Perceive the Benefits of Integrating ChatGPT into Their Writing? Utilizing ChatGPT in their various writing process components, the students reported that ChatGPT had the following benefits: (1) accelerating their writing process, (2) easing their cognitive load, (3) fostering new learning opportunities, (4) getting immediate feedback, and (5) promoting positive feelings about writing. [SEP] Justify:

Output: The text corroborates the claim by illustrating how students utilize ChatGPT to enhance their writing processes, which reflects the personalized learning experience AI offers. For instance, Lydia's iterative revision process with ChatGPT highlights its adaptability to her needs, as she sought specific feedback on structure and argument. Similarly, other students reported using the tool to address both global level issues in their writing and specific sentence-level enhancements, indicating that AI is effectively tailoring its support to accommodate diverse student needs. Moreover, benefits such as easing cognitive load and providing immediate feedback align with the concept of personalized learning experiences, confirming that AI is indeed being utilized to adapt to the individual requirements and performance of the students.


Input: Task: contrast the claim using the given text. Claim: Personal AI assistants will be able to understand emotions as well as humans by next year. [SEP] Text: PostLog inSign upPost8PostConversation2GrantsPeer ReviewsDocumentFlag content 3,000 Tipped ≈ $0. 008Dr. Barrett: How to 18, 2023DOI10. 55277/ResearchHub. fke8bs6uSaveTipDocumentFlag content8TipSaveDocumentFlag contentPostConversation2GrantsPeer ReviewsKey takeawaysExplore the challenge of defining the fundamental components of emotions and the absence of exclusive physical indicators. (Segment 1)Dispute the notion that emotions universally manifest on people's faces, highlighting the significant influence of cultural and environmental factors. (Segment 3)Demonstrate the impact of culture on the interpretation of facial expressions and raise questions regarding its legal implications. (Segment 5)Investigate how language constrains our ability to express emotions and advocate for embracing ethnic diversity for better understanding. (Segment 6)Examine the evolving role of the brain in labeling emotions and illustrate how this evolution impacts our perception. (Segment 8)Advocate for flexible emotional regulation and underscore the value of embracing pain for personal growth. (Segment 14)Discuss strategies to enhance sensory and emotional experiences, with a particular emphasis on improving understanding and perception. (Segment 16)Explore the intricate connection between the body and mind, examining sensory systems, motor control, and emotional regulation. (Segment 18)Delve into the relationship between digestive factors, SSRIs, and mental health, with the insights of Andrew Huberman. (Segment 19)SummaryDr. Barrett, obtained a Doctor of Philosophy degree and currently occupies the esteemed position of of Psychology at Northeastern University. Dr. Barrett possesses a high level of experience in the domain of emotional science, which has garnered her widespread acclaim and international recognition for her extensive knowledge and significant achievements in this subject. The author provides a comprehensive explanation of the notion of emotions and delves into the brain's function in processing and integrating signals derived from both internal physiological sensations and external surroundings, hence resulting in the formation of unique emotional experiences for each individual. The forthcoming discourse will center on the analysis of the relationship between emotions and language, the impact of linguistic specificity on the processing of emotions, the importance of facial expressions in the domain of emotions, and the interconnectedness of emotions with other factors such as sleep, movement, nutrition, and the formation and reinforcement of social connections. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexity of defining emotions, the role of cultural and environmental factors in emotional expression, and how language limits the expression of emotions. This indicates that understanding emotions is not a straightforward task and suggests significant challenges remain before AI can understand emotions as well as humans.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: Our work to date is mainly descriptive, and it requires more explicit experimental approaches to generate robust conclusions about the evolution of this golden aposematic signal. For example, the relationship between the level of unpalatability and the quality of the golden signals needs rigorous testing with a greater number of mimics. This would also help in deciphering the role of less-defended mimics in the protection of the entire complex. Similarly, exploring the function of non-visual signals in deterring predators will elucidate how this mimicry ring works. In conclusion, we present the first account of the golden mimicry complex. It consists of species that show considerable variation in their level and mode of defence, ranging from undefended Batesian mimics to highly defended Müllerian mimics, with most members of the complex somewhere in between. Our data suggest that all members of the golden mimicry complex receive quite similar net protection from a variety of predators presumably because they have a variety of defensive traits. We predict that mimetic complexes that incorporate a range of different forms of defence are not only more common than previously believed, but also more likely result in mutualism among the unequally defended members of the complex. Materials and methods Description of the mimetic complex Request a detailed protocol Arthropod species forming the complex were selected based on the presence of a golden colour pattern on the dorsal side of their body and body size falling within the range 3–15 mm. ants, and known ant-like mimics, were examined for a dorsal golden appearance. The specimens we examined were either field collected by the authors (outback, NSW eastern coast, Queensland eastern coast) or from museum collections (Australian Museum, Sydney). All other specimens we viewed were images from AntWiki (www. antwiki. org), Atlas of (www. ala. org. au), or taxonomic papers (Ogata and Taylor, 1991; Shattuck and Barnett, 2006; McArthur, 2007; Kohout, 2010a, 2010b, 2012, 2013a, 2013b; Heterick and Shattuck, 2011; Casis and Symonds, 2012; Shattuck and Marsden, 2013). The dorsal colour pattern was estimated from digital images of specimens (Figure 7) as performed in other studies on mimetic complexes (Wilson et al. , 2012, 2015; Rodriguez et al. , 2014). [SEP] Justify:

Output: The text does not address the claim about the intelligence of ants. Instead, it discusses a study on mimicry complexes involving ants and their defensive traits, along with descriptions of their coloration and collection methods. As such, it does not provide support or refutation regarding the claim of ants' intelligence.


Input: Task: corroborate 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: Students stated that using ChatGPT could “speed up the process of writing” (Alex, Interview) as exemplified by the following quotes: “ChatGPT really helped me to explore the essay topics that I’m interested in within a very short amount of time and identify what can be written about” (Nora, Interview); “I discovered after using it for my final essay that ChatGPT can greatly improve the efficiency of my writing” (Alex, Reflection). For L2 writers, it significantly saved the time they typically spent on editing, as mentioned by Lydia: As an international student who is not a native English speaker, writing college essays would take me double the amount of time compared to those who write essays in their first language. Oftentimes, the biggest time I spent was on editing the grammar and trying to make my language readable and understandable. (Lydia, Reflection) The benefits of saving the time and energy on language concerns, grammar, wording, and the organization of ideas and messy drafts, furthermore, reduced the cognitive burden among the student writers, both L1 and L2. For instance, knowing ChatGPT’s editing power, Alex felt that he was able to “focus more on the subject of the writing rather than the language itself” and “spew out thoughts freely” when drafting the essay (Alex, Interview). Likewise, the L2 students noted that ChatGPT allowed them to delay their concerns about the linguistic forms of ideas and alleviate the demanding cognitive load associated with L2 writing. As claimed by Lydia, “It freed my thoughts so that I could spend more time revising the content, but not worry about how to express my ideas for the essay” (Lydia, Interview). The students conveyed that incorporating ChatGPT in different components of writing also fostered new learning opportunities for them to improve writing. Nora shared that “ChatGPT not only made my language more fluent and comprehensible, but it also helped me to learn new ways of expression in English” (Nora, Interview). Su remarked that although ChatGPT’s feedback was generic, it promoted her to do further research about her topic and learn more writing strategies (Su, Written Assignment). Students particularly highlighted the “instant and personalized feedback” (Kevin, Reflection) provided by ChatGPT as a strong impetus and benefit. [SEP] Justify:

Output: The text corroborates the claim by providing multiple examples of how students are using ChatGPT to personalize their writing experiences. Students express that ChatGPT not only improves their writing efficiency but also allows them to focus more on the subject matter rather than language concerns. For instance, Alex mentions being able to 'focus more on the subject of the writing' thanks to ChatGPT, while Lydia states it 'freed my thoughts' to revise content without worrying about expression. Furthermore, students noted the 'instant and personalized feedback' from ChatGPT, indicating its adaptability to student needs and performance, thereby supporting the claim that AI is used to enhance personalized learning.


Input: Task: contrast the claim using the given text. Claim: Personal AI assistants will be able to understand emotions as well as humans by next year. [SEP] Text: The attendees will also partake in a discussion concerning practical approaches for coping with feelings of uncertainty, as well as methods for improving one's understanding of the emotional encounters of others. This specific episode is anticipated to engage persons who possess a curious disposition toward the complex interplay between neuroscience and psychology that underlies the phenomenon of emotions. Furthermore, it possesses significance for those who want to augment their self-awareness, interpersonal connections, and overall capacity to navigate the world with increased flexibility. 1. 00:05:46 of EmotionsDr. Barrett explores the complex endeavor of distinguishing and detecting emotions across the span of the last 150 years. This observation underscores the difficulty in establishing a precise and exclusive set of fundamental elements for emotions. These elements, such as physiological changes in the body, brain activity, and facial expressions, are not inherently unique to emotions and can occur in various situations. Researchers have consistently exerted considerable efforts in their endeavor to establish a comprehensive description of emotions, employing diagnostic patterns as a basis for their investigations. These patterns entail the identification of unique physiological signs, such as increased heart rate and distinctive facial expressions, which were hypothesized to be linked to particular emotions, such as fear. However, the assertions put forth lacked empirical support, therefore leading to perplexity around the perception of different emotions in the absence of observable physical cues. Researchers have been intrigued by this enigma for a significant period [1]. Figure 1. to Expressions of Fear and Surprise [1]. 1. Zhao, k. , et al. , to Expressions of Fear and Surprise. Frontiers in Psychology, 2017. 8. 2. 00:10:42 & Interpretation, Emotion Dr. Barrett offers a critical analysis of the conventional conceptualization of an emotion system and a facial expression system. Dr. Barrett argues that while there are processes that enable the movement of facial muscles, it is crucial to acknowledge that not all facial movements may be considered as indications of internal emotional states. The author pays much attention to the attribution of significance to facial movements, underscoring the importance of interpretation and contextual elements. Furthermore, the author emphasizes the role of the human brain in the cognitive processing of various sensory inputs, which is crucial for the understanding and interpretation of emotions. The analysis of facial expressions relies on inferential reasoning within the wider framework of an individual's specific circumstances, rather than being solely reliant on direct observation. [SEP] Justify:

Output: The text contrasts the claim by describing the complexities and ongoing challenges in understanding emotions, as highlighted by Dr. Barrett's analysis. It points out that establishing a precise and exclusive set of fundamental elements for emotions is difficult and that researchers have struggled with empirical support for their assertions. The mention of the importance of the human brain’s cognitive processing, interpretation, and contextual elements in understanding emotions suggests that personal AI assistants will face significant hurdles in achieving human-like emotional understanding, particularly within the next year.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: Field collected specimens (of 41 species) were killed by ethylacetate and arranged in their natural body position. Photographs were taken with a HF G10 against a white background and lit from above by two full spectrum light bulbs (2. 0 UVB, Eko Terra). Photographs of the remaining species (59 species) were obtained mostly from the personal collection of R. Kohout, followed by AntWiki, and Atlas of Living Australia. Prior to taking measurements all photographs were calibrated for the white and/or black colour within ImageJ to correct for different lighting conditions. The average value of each RGB colour component was estimated on the following body parts: dorsal side of head, appendages (femur II), pronotum, mesonotum, metanotum, petiole, first segment of the gaster, and the remaining segments of the gaster/abdomen in ants and wasps using ImageJ. Likewise, for hemipterans and spiders the average colour value of each RGB colour component of corresponding body parts (to that of ants) were examined including the pedipalps/chelicera, frontal third of prosoma, second third and last third of the prosoma, legs (femur III), the first half of the abdomen and the second half of the abdomen. In each species, we also measured the length of each body part (listed above) and an absolute area of the golden colouration on the dorsal side using ImageJ. Figure 7 Download asset Open asset Selected list of 100 species representing the ten putative mimetic rings. Species are arranged within each ring from the most to the least unpalatable (left to right). Some pictures of ants are displayed with the permission of AntWiki and R. Kohout. To assess colour pattern similarity among mimics and to find how many rings there are, we subjected 24 measures of the colour pattern, body size and area of golden patch of 100 mimics (Pekár et al. , 2016) to the Non-Scaling based on Bray-Curtis distance matrix. Because various body parts have different area and are thus differently conspicuous to predators, we used weighting of the measures by their relative size. Then we estimated the number of putative rings via k-means clustering using the vegan R package (Oksanen et al. , 2013). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the methodology used to assess color pattern similarities and body measurements of various ant species, without making any assertions about intelligence. Therefore, it does not support or contradict the claim that ants are the most intelligent species.


Input: Task: corroborate 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: For instance, as a frequent visitor of the school’s writing center, Lydia mentioned she typically scheduled two to three appointments with a writing tutor for each major writing assignment she had worked. With ChatGPT, she could obtain feedback anytime: “don’t have to physically go to the writing center at 11 pm, waiting for the previous visitor to finish their session” (Lydia, Interview). She used “my walking AI tutor” to describe the role of AI in her writing. Ultimately, the students mentioned that these cognitive and practical benefits of ChatGPT not only improved their efficiency of writing, but also promoted positive feelings about writing. They used words such as “more relieved” (Emma), “sense of accomplishment” (Lydia), and “less anxious” (Nora) to describe the AI-assisted writing process. Although the students expressed different needs and utilization of ChatGPT, they all conveyed that they would like to continue using it in the future. 5. 3 and to Assist with Their Writing? Despite the benefits and usefulness of ChatGPT for assisting with students’ writing, they also expressed many reservations and limitations regarding the AI tool. The first concern was about the false information it produced and its potential to mislead people. The students commented that ChatGPT tended to “make up information” (Emma), “make assumptions and guesses” (Su), and generate “inaccurate information” (Nora), “wrong information” (Alex), and “nonsense” (Lydia). Furthermore, the students pointed out that ChatGPT was inadequate in addressing high-level questions requiring critical thinking, as Su explained: “was consulting with ChatGPT, I learned that it has a very limited understanding of the topic I was talking about” (Su, Reflection). Other students also pointed out that the responses they got from ChatGPT could be “very generalized” (Kevin) and lacked “depth and complexity” (Nora). The next shortcoming of ChatGPT, as noted by the students, is the lack of creativity and originality. [SEP] Justify:

Output: The text corroborates the claim by detailing how students utilize AI, specifically ChatGPT, to enhance their writing skills through personalized feedback and support. The description of Lydia using ChatGPT to avoid traditional constraints of scheduling tutoring sessions aligns with the idea of AI adapting to student needs and performance. Furthermore, the improvements in writing efficiency and the students' expressed desire to continue using the AI tool highlight its role in personalizing their learning experiences.


Input: Task: contrast the claim using the given text. Claim: Personal AI assistants will be able to understand emotions as well as humans by next year. [SEP] Text: Dr. Barrett also acknowledges the presence of a mutual connection between the face and the brain, albeit this interaction is not limited to emotional functions but rather forms part of the broader communication network between the brain and the body. This essay presents a counterargument against the concept of a reductionist and mechanistic relationship, suggesting that specific facial expressions do not directly elicit or generate distinct emotional states. This is supported by the absence of a segregated emotional system within the brain [2]. Figure 2. Functional brain networks associated with emotion processing [2]. 2. Canu, E. , et al. , Resting state functional brain networks associated with emotion processing in frontotemporal lobar degeneration. Molecular Psychiatry, 2022. 27(11): p. 4809-4821. 3. 00:19:33 & Emotion, IndividualizationDr. Barrett offers a comprehensive examination of her scientific trajectory, while also expressing her reservations regarding the conventional perspective on the relationship between facial expressions and emotions. Dr. Barrett elucidates that the concept of universal facial expressions associated with specific emotional states is predominantly grounded in Western stereotypes that have been solidified as empirical truths. The initiation of her scientific endeavor occurred when she endeavored to use this information in a controlled experimental context, only to confront a disparity between the theoretical understanding and her empirical findings. Within the discipline of psychology, there has been a continuous discourse about the fundamental characteristics of facial expressions and their potential universality. Consequently, scholars have endeavored to establish a consensus by synthesizing the existing body of literature related to this topic. Dr. Barrett, together with a team including four esteemed senior scientists, undertook a comprehensive examination of a substantial compilation consisting of more than one thousand scientific 3. to Learning [3]. 3. Hooker, C. I. , et al. , to Learning. 2006. 26(35): p. 8915-8922. 4. 00:31:03 Emotion Categories, Culture & Dr. Barrett's in podcast with primarily focuses on the complicated dynamics of emotions, facial expressions, and their complex interaction with cultural factors. Dr. Barrett highlights the concept that emotions should not be regarded as distinct entities, but rather as categorizations or groupings of events, and even as behaviors underlining the dynamic and changeable nature of emotional experiences. In his scholarly discourse, Dr. Barrett offers an alternate viewpoint that challenges the prevailing consensus on the inherent capacity of newborns to see and comprehend facial expressions. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities of emotions and their relationship with facial expressions, as articulated by Dr. Barrett. It suggests that emotions are not distinct entities and that the understanding of emotions involves a dynamic interplay that is not reducible to facial expressions alone. This implies that personal AI assistants may struggle to achieve human-like emotional understanding in such a timeframe, given the ongoing debates about the nature and comprehension of emotions.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: This method used the simple structure index (ssi) as a partition criterion (Legendre and Legendre, 1998) as AIC is not available. Blue (RBG) contrast was estimated using 55 species of mimics. It was computed as the sum of differences in red, green, and blue values representing the golden and black colouration found on the body of mimics. The phylogenetic relationship at the genus level among the golden mimics was reconstructed from a combination of published phylogenies (Moreau et al. , 2006; Regier et al. , 2010; Robson et al. , 2015; Pekár et al. , 2017). If species level phylogeny was not available, we used polytomic splitting at the species level. To test for phylogenetic inertia of classification to putative rings we used generalised least squares (GLS) with Pagel’s phylogenetic correlation structure (Paradis, 2006), in which low value of λ indicates low phylogenetic signal. The mode by which the animals generated the golden colour (pigments or structural colours) was determined via microscopy. One specimen of each species from each genus (Ephutomorpha aurata, Camponotus aeneopilosus, Daerlac nigricans, Dolichoderus doriae, Polyrhachis ammon, P. vermiculosa, Ligonipes illustris, Myrmarachne luctuosa, Myrmecia tepperi, Eurymela rubrolimbata) was further examined via SEM. The golden gaster/abdomen was dried at a room temperature for 10 days, then mounted on a stub and coated with gold prior to viewing under a 648OLA. The presence of pigments on the gaster/abdomen was examined in ethanol-preserved specimens under Olympus stereomicroscope SZX9 and photographed with a digital camera mounted on the stereomicroscope. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses methods of analyzing species characteristics, specifically focusing on color patterns and phylogenetic relationships of certain insects, without addressing the intelligence of ants or any other species. There's no indication or evidence supporting or contradicting the claim about ants being the most intelligent species.


Input: Task: corroborate 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: It gives a shortcut for people who do not want to make the effort to learn and think. But it could be useful for those who really want to learn” (Lydia, Interview). Alex noted that to prevent the loss of learning opportunity, for instance, he decided that rather than taking ChatGPT’s diction suggestion immediately, he “would use those words in the next essay,” demonstrating his effort in learning and internalizing the knowledge. In general, the students were still exploring ways to use ChatGPT in critical, authentic, and ethical ways that would promote rather than harm their learning. 6 DiscussionAdopting sensemaking theory, the study investigated how students made sense of their AI-assisted writing practices, providing insights into students’ learning process and their shared practices emerging around the AI technology. Confirming previous research (e. g. , Guo et al. , 2022; Holmes et al. , 2019; Su et al. , 2023), this study found that the students overall had positive experiences with generative AI-assisted writing, for it could accelerate their writing process, reduce their cognitive load and anxiety, and provide prompt feedback. The students integrated ChatGPT into various components of their composing process, such as searching for content, ideational planning, language editing, and revising. Although the students acknowledged the cognitive and affective benefits (e. g. , Ebadi & Amini, 2022; Fryer & Carpenter, 2006) of using ChatGPT in writing, they were very cautious about adopting its ideas and suggestions at different discourse levels (i. e. , essay, paragraph, and sentence levels) due to critical, ethical, and authentic concerns. This finding extends previous research which identified that students’ primary concerns were academic dishonesty and educational inequity (Yan, 2023). Despite recognizing AI’s limitations such as the lack of in-depth insights (Gao et al. , 2022), originality, creativity, and reliability—qualities essential for good academic writing—the students deemed it necessary to embrace rather than abandon the tool, with the purpose of fostering one’s critical thinking and writing skills. [SEP] Justify:

Output: The text corroborates the claim by discussing how students are using AI tools like ChatGPT to enhance their learning experiences. It describes how students are integrating AI into their writing process, which includes aspects such as ideational planning, language editing, and revising. Moreover, the students are shown to be actively engaging with the technology in ways that promote learning, as reflected in the quote about using ChatGPT's diction suggestions to improve their essays. The emphasis on students’ positive experiences and the benefits, such as reducing cognitive load and providing prompt feedback, aligns directly with the idea that AI can personalize learning according to the students' needs.