Input: Task: pass the claim using the given text. Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: What astrometric and photometric accuracy can be achieved? What level of automation can be achieved in the precovery workflow? What are the major challenges to exploiting various astronomical surveys for NEO space safety purposes? What value do these precoveries have for planetary defence? Do precoveries have benefits for NEO science? The structure of the paper is as follows. Section 2 describes the data and lists of NEOs used for precovery. In Sect. 3, the pipeline is described in detail. In Sect. 4, the findings and results of NEO precovery are presented. In Sect. 5, we summarize and conclude with the main results of this work and discuss the next steps4. 2 Data, software systems, and applications In this work, we use the archival imaging data of OmegaCAM, the wide-field imaging camera of the VLT (VST) at ESO’s Observatory. In the past OmegaCAM was used for several wide-field surveys including the Kilo-degree Survey (KiDS; Kuijken et al. 2019), the VST-ATLAS surveys (Shanks et al. 2015), the Survey (FDS; Peletier et al. 2020), and VEGAS (Iodice et al. 2021). OmegaCAM has 32 science CCDs with a field of view (FoV) of about 1o × 1° on the sky. In its first decade of science operation, OmegaCAM covered a large fraction of the southern hemisphere (Fig. 1). Considering the large volume of the dataset of OmegaCAM, we developed a pipeline to automatize the process of (p)recovery. Here we focus our (p)recovery effort on two datasets of NEOs. First, the NEO risk list5. This list is provided by the (ESA-NEOCC) and consists of about 1350 known NEOs with a non-negligible chance of impact in the next hundred years. This list is updated regularly based on the most recent observations. We use the list available on 1 February 2022. Second, the NEO full list. This list consists of all the known NEOs (about 30 000 sources). We used the version available on 1 June 2022. These data are processed and analysed using several public web tools to collect the predicted properties of NEOs, using the system (Begeman et al. [SEP] Justify:

Output: The text does not address the use of data mining techniques for classifying astronomical objects but instead discusses the capabilities and methodologies regarding NEO (Near-Earth Object) precovery efforts and the associated datasets. There is no mention or implication of data mining applied to classify astronomical objects.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Intelligent conversational agents in patient self-management: a systematic survey using multi data sources. Technol Inform. Aug 21, 2019;264:1813-1814. [CrossRef] [Medline] Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Ment Health. Mar 2011;38(2):65-76. [FREE Full text] [CrossRef] [Medline] Improving the well-being of adolescents with type 1 diabetes during the COVID-19 pandemic: qualitative study exploring acceptability and clinical usability of a self-compassion chatbot. JMIR Diabetes. May 05, 2023;8:e40641. [FREE Full text] [CrossRef] [Medline]Anmella G, Sanabra M, Primé-Tous M, Segú X, Cavero M, Morilla I, et al. Vickybot, a chatbot for anxiety-depressive symptoms and work-related burnout in primary care and health care professionals: development, feasibility, and potential effectiveness studies. J Res. Apr 03, 2023;25:e43293. [FREE Full text] [CrossRef] [Medline]. Artificial intelligence-based chatbot for anxiety and depression in university students: pilot randomized controlled trial. . Aug 12, 2021;5(8):e20678. [FREE Full text] [CrossRef] [Medline] A therapeutic relational agent for reducing problematic substance use (Woebot): development and usability study. J Res. Mar 23, 2021;23(3):e24850. [FREE Full text] [CrossRef] [Medline]. A beta-prototype chatbot for increasing health literacy of patients with decompensated cirrhosis: usability study. . Aug 15, 2023;10:e42506. [FREE Full text] [CrossRef] [Medline]. kBot: knowledge-enabled personalized chatbot for asthma self-managemen. In: Proceedings of the 2019 on Smart Computing. Presented at: '19; June 12-15, 2019, 2019;138-143; Washington, DC. URL: [CrossRef]Milne-Ives M, de de Pennington N, Mole G, et al. The effectiveness of artificial intelligence conversational agents in health care: systematic review. J Res. [SEP] Justify:

Output: The text corroborates the claim by listing several studies and examples where AI-powered chatbots, such as Vickybot and Woebot, are developed to provide support for anxiety, depression, and other mental health issues. It highlights their usefulness in various patient engagement scenarios, including self-management during the COVID-19 pandemic and improving the well-being of adolescents with type 1 diabetes.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: 6A The F1 score of 7 tools on “single-strain” simulated datasets under different sequencing depths. B Running time comparison of 7 tested tools. Sigma and Pathoscope2 have no values on some datasets because they are too computationally expensive to construct databases for the corresponding bacteria or to identify strains from simulated readsFull size imageDetecting a reference strain from simulated readsThe purpose of this experiment is to test StrainScan and other tools for identifying the present strain in a sample. There are two challenges. The first challenge is to distinguish the true strain from other highly similar peers. The second challenge is to identify a strain with low depth. Thus we generated multiple datasets with different sequencing depths. For each bacterium, we randomly picked a reference strain and used its simulated short reads as input to all tools. In order to avoid any data-related bias, we repeated the experiment 60 times, with a strain randomly picked each time. For P. copri, we only repeated the experiment 50 times because it has a small number of genomes. For each selected strain, we simulated reads with different sequencing depths (10X, 5X, 3X, 1X). Thus, there were 1400 datasets in total. For each dataset, we simulated Illumina reads using ART [57] with the following parameters: -p -l 250 -f depth -m 600 -s 150, where depth is the specified sequencing depth. StrainScan and the other six programs were used to identify strains from these simulated reads. and Pathoscope2 are computationally expensive, we were not able to construct their databases for E. coli, S. epidermidis, and M. tuberculosis. The F1 score of each program is shown in Fig. 6A. recall, and precision are recorded in S4. Of these selected strains, some of them have more than 99. 5% k-mer-based Jaccard similarity with at least one other reference strain genome. As a result, several tools have low F1 scores. StrainScan achieves near-perfect F1 scores on all datasets when the depth is higher than 1X. However, for the species containing highly similar strains (A. muciniphila and M. tuberculosis), the F1 score of StrainScan drops a little when the depth is 1X. [SEP] Justify:

Output: The text contrasts the claim by explaining the challenges of identifying strains in microbiome samples, where it is noted that 'several tools have low F1 scores' due to high similarity between strains. This suggests variability in microbiome composition, as the presence of strain similarity indicates that individuals may have distinct microbial communities rather than identical compositions.


Input: Task: pass the claim using the given text. Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: This database was updated with observations until approximately 1 November 20218. Fourth, the ephemerides database in point 2 was cross-matched in the three-dimensional space of (observation date, RA, Dec) by SSOIS itself with the internal database of OmegaCAM observations in point 3. The cross-match algorithm is described in Gwyn et al. (2012). Fifth, uncertainties in input orbital parameters and image pointings are not taken into account in the cross-match procedure. This means that the cross-match only produces a result when an NEO ephemeris for the exact orbital elements specified in step one falls inside the field of view (FoV) of an OmegaCAM exposure for the exact (observation date, RA, Dec) specified in step three. Sixth, we did not ask SSOIS to refine the cross-match to the (X, Y) of a specific detector as that functionality is not available (yet) for OmegaCAM. To automate our usage of SSOIS we implemented a scripted interface to SSOIS. This interface submits a query to SSOIS. In return, SSOIS provides a list of (raw) imaging data (exposures) for OmegaCAM, date, time, exposure time, and the observed filter. SSOIS outputs for OmegaCAM point to the main raw frames, and do not specify which extension (chip) overlaps with the predicted position of the NEO. The information returned by SSOIS on NEO names and OmegaCAM exposures was stored in a local SQLite file. 3. 2 Final spatiotemporal cross-match (step 2) 2, the pipeline uses the NEO (NEODyS)9 and JPL Horizons10 web service11 to obtain more accurate predictions of the NEO positions over the duration of exposure and to obtain the predicted angular motion and visual magnitude. We supply to NEODyS/Horizons the start and end time of the observation, the name of the NEO, and the observatory identifier to specify its location. This query to NEODyS/Horizons returns the (RA, Dec), 1σ uncertainties in position, V-band magnitude and the position of the NEO at the beginning and end of the exposure. These values are used to predict the rate and direction of NEO motion relative to the astrometric reference frame. [SEP] Justify:

Output: The text does not directly address the use of data mining techniques for classifying astronomical objects. Instead, it discusses the processes related to cross-matching observations and querying data from specific services without mentioning classification methodologies or data mining techniques. Thus, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Oct 22, 2020;22(10):e20346. [FREE Full text] [CrossRef] [Medline] Conversational agents for chronic disease self-management: a systematic review. . 2020;2020:504-513. [FREE Full text] [Medline]. Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review. JMIR Cancer. Nov 29, 2021;7(4):e27850. [FREE Full text] [CrossRef] [Medline]Shah SS, Gvozdanovic A. Digital health; what do we mean by clinical validation? Med Devices. Dec 12, 2021;18(sup1):5-8. [CrossRef] [Medline]. Artificial intelligence-based conversational agents for chronic conditions: systematic literature review. J Res. Sep 14, 2020;22(9):e20701. [FREE Full text] [CrossRef] [Medline]. Digital health technologies: opportunities and challenges in rheumatology. Rheumatol. Sep 24, 2020;16(9):525-535. [CrossRef] [Medline] Examining patient engagement in chatbot development approaches for healthy lifestyle and mental wellness interventions: scoping review. J Particip Med. May 22, 2023;15:e45772. [FREE Full text] [CrossRef] [Medline] Design and development of a person-centered patient portal using participatory stakeholder co-design. J Res. Feb 11, 2019;21(2):e11371. [FREE Full text] [CrossRef] [Medline] Le « Montreal model » : enjeux du partenariat relationnel entre patients et professionnels de la santé. Santé Publique. 2015;1:41-50. [FREE Full text] [CrossRef]. Rapport de la Déclaration de Montréal pour un développement responsable de l'intelligence artificielle. Raisons Politiques. 2020;77(1):67-81. [FREE Full text] [CrossRef]-Savary D, Gagnon J, St-Pierre M, Rhainds M, et al. Introducing patients' and the public's perspectives to health technology assessment: a systematic review of international experiences. . Jan 2011;27(1):31-42. [SEP] Justify:

Output: The text supports the claim by referring to various studies and systematic reviews that focus on the application of chatbot technologies for health-related purposes, including chronic disease self-management and mental wellness interventions. Specifically, it mentions an examination of patient engagement in chatbot development approaches aimed at healthy lifestyles and mental wellness, which directly correlates with the claim about AI-powered chatbots providing mental health support.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: tuberculosis, StrainScan assigned most of the strains to one big cluster with a significant number of k-mers (Supplementary Fig. S6), and thus StrainScan took more time to distinguish them. Nevertheless, StrainScan still has the best recall and precision in terms of the identification of M. tuberculosis strains. All the strain identification experiments were tested on an 6. 8 node with 2. 4Ghz 14-core E5-2680v4 CPUs and 128 GB memory. We used 8 threads for all tools. In summary, StrainScan is able to achieve higher precision without sacrificing resolution, even when the true strain has peers of high sequence similarity. Detecting co-existing strains from simulated dataIt has been shown that human-associated microbiota is often a complex mixture of closely related strains of the same species [15]. To quantitatively compare the performance of and StrainScan on identifying multiple strains of the same species, we generated simulated datasets containing 2, 3, and 5 randomly selected strains from six bacteria. and Pathoscope2 took too long to process these datasets, they were not included in this experiment. To investigate how the similarities between the strains affect the tool’s performance, we used two strategies in the selection of multiple strains. During the clustering step of StrainScan, strains with k-mer-based Jaccard similarity greater than or equal to 95% (corresponding to an approximate ANI of 99. 89%) are grouped into the same cluster. Therefore, it is more difficult to identify and distinguish the co-existing strains that are in the same cluster than those from different clusters. To consider different levels of difficulty, our first strategy randomly picked strains from different clusters while the second strategy selected different strains from the same cluster. For each strategy, we randomly selected 2, 3, and 5 strains (3 groups) and simulated the short reads using different coverage profiles: 100X and 10X for 2 strains, 100X, 50X, and 10X for 3 strains, and 100X, 70X, 50X, 20X, and 10X for 5 strains. Other read simulation parameters are the same as the “single-strain” experiment. Then we repeated the experiment 10 times by choosing another group of strains. [SEP] Justify:

Output: The text contrast with the claim by indicating that 'human-associated microbiota is often a complex mixture of closely related strains of the same species'. This suggests that variations exist within microbiomes, rather than everyone having an identical composition.


Input: Task: pass the claim using the given text. Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: Based on the returned apparent magnitude we make a preliminary prediction on the signal-to-noise (S/N) for the given filter and exposure time of the candidate (p)recovery observation by assuming its appearance is a point source (i. e. neglecting proper motion) and assuming a solar spectral energy distribution of the NEO. Candidate (p)recoveries for which NEODyS/Horizons confirm the overlap with the exposure FoV and that also have a predicted S/N > 1 are stored in the local SQLite database. To automate our usage of NEODyS/Horizons we implemented a scripted interface to NEODyS/Horizons, using the same approach as for SSOIS. Fig. 2Four-step precovery pipeline. The pipeline gets a text file containing NEO names as the input and produces a CSV table at the end containing the NEO precoveries. The intermediate outputs are stored in an SQLite database, which communicates with the pipeline in many instances throughout the process. 3. 3 Data processing and astrometry (step 3) In step 3, we apply several filters and criteria to the precovery candidates resulting from step 2 to narrow down the search to sources that are likely to be detectable. These filters are described below. An upper limit on the angular separation (RA, Dec) between SSOIS and NEODyS/Horizons predictions; An upper limit on the 1σ uncertainty in the position (RA, Dec); Availability of the raw data (publicly available); The exact position of NEOs on the camera; A lower limit on the predicted S/N. The imaging data used for precovery were identified by SSOIS and were based on its predictions. However, once we refine these predictions using NEODyS/Horizons, the images may not overlap with the refined position. Therefore, if the angular separation between these predictions is larger than a certain limit (for example larger than the FoV), we might assume that the object is not within the FoV of the camera. Then, for cases where the uncertainties in position are too large, the possibility that the NEO is within the FoV is very small. The pipeline skips these frames. Additionally, given the coordinates of the object, the pipeline inspects which CCD is overlapping with the corresponding position of NEOs and if there is no overlap, the pipeline excludes these frames. [SEP] Justify:

Output: The text does not directly address the claim regarding the use of data mining techniques in classifying astronomical objects. It describes a specific process for candidate recovery observations of NEOs, detailing steps and criteria for filtering and storing data, but it does not mention classification or data mining explicitly. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: AbstractThe global mental health crisis underscores the need for accessible, effective interventions. Chatbots based on generative artificial intelligence (AI), like ChatGPT, are emerging as novel solutions, but research on real-life usage is limited. We interviewed nineteen individuals about their experiences using generative AI chatbots for mental health. Participants reported high engagement and positive impacts, including better relationships and healing from trauma and loss. We developed four themes: (1) a sense of ‘emotional sanctuary’, (2) ‘insightful guidance’, particularly about relationships, (3) the ‘joy of connection’, and (4) comparisons between the ‘AI therapist’ and human therapy. Some themes echoed prior research on rule-based chatbots, while others seemed novel to generative AI. Participants emphasised the need for better safety guardrails, human-like memory and the ability to lead the therapeutic process. Generative AI chatbots may offer mental health support that feels meaningful to users, but further research is needed on safety and effectiveness. Similar content being viewed by others The health risks of generative AI-based wellness apps 4. Access to care remains limited, with for example only 23% of individuals suffering from depression receiving adequate treatment in high-income countries, while in low- and middle-income countries, the figure drops to a mere 3%5. Digital mental health interventions (DMHIs) have emerged over the last decade as a promising potential response to the treatment gap, leveraging technology to deliver low-cost, effective, always-available and anonymous (and thus low-stigma) mental health treatment at scale6. Typically delivered through mobile apps and websites, DMHIs encompass a range of tools including psychoeducation, mood tracking, mindfulness, journalling, peer support and digital cognitive behavioural therapy (CBT) programs7. However, the evidence for the effectiveness of DMHIs has been limited, with a meta-analysis of randomised controlled trials (RCTs) finding only small effect sizes, potential publication bias, and a lack of active controls8,9,10. Moreover, user engagement remains a persistent challenge, with mixed user reviews11, and studies indicating that 30 days after installation the proportion of users still active may be as low as 3%12. [SEP] Justify:

Output: The text corroborates the claim by discussing how generative AI chatbots, such as ChatGPT, are emerging as novel solutions for mental health support. The participants in the study reported high engagement and positive impacts from using these chatbots, indicating that they felt supported and experienced emotional healing. The text mentions themes such as ‘emotional sanctuary’ and ‘insightful guidance’ provided by the AI chatbots, which aligns with the claim that they can provide mental health support and enhance patient engagement.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Ultimately, for each bacterial species, we generated 30 sets of data containing different numbers of strains using the first and the second strategies, for a total of 60 sets of data. So there were a total of 360 (60 × 6) simulated datasets for the six bacterial species. Cluster-level performance evaluation for multi-strain cases If a sample contains n strains from the same cluster defined by StrainGE or StrainEst, only one representative strain will be returned based on these tools’ design. Using this one representative strain will lead to a very small recall for multi-strain experiments. To avoid that, the returned representative strain will be counted n times, which usually makes the recall 1. 0 in these tools’ favor. Because our clusters have greater granularity than the ones defined by StrainGE and StrainEst, the samples that are simulated from strains of the same cluster all belong to this case. Fig. 7A The F1 score of 5 tools on “multiple-strain” simulated datasets. The “cluster” in the title refers to the clusters generated by the CST algorithm. There are 60 sets of simulated reads containing 2, 3, and 5 strains with different similarities for each bacterial species. Note that StrainSeeker is not able to identify strains of M. tuberculosis and therefore, the related scores are 0. B Running time comparison of 5 tested toolsFull size imageBenchmark results The F1 score comparison of different tools is shown in Fig. 7A. recall, and precision are recorded in S5 and S6. As shown in Fig. 7A, StrainScan achieves a near-perfect F1 score on all tested datasets. StrainGE has the same cluster-level F1 scores as StrainScan for experiments on P. copri and E. coli. Its cluster-level F1 score generally decreases with the increase of the similarity of the input strains, which can be observed by comparing the left and right panels of Fig. 7A. For example, its cluster-level recall on 5-strain datasets of A. muciniphila dropped from 0. 94 to 0. 42 (S6). Similar to single-strain experiments, StrainGE’s cluster-level performance on M. tuberculosis is still poor. StrainEst has many FPs in its output, which leads to low precision and F1 score. [SEP] Justify:

Output: The text contrasts the claim by discussing the variability in microbiome composition, as evidenced by the generation of 360 simulated datasets with different strains. It highlights that samples are considered from different clusters based on strain similarities, indicating that individual microbiomes are not identical but instead diverse, which is supported by metrics like F1 score performance for varying input strains. The reference to tools designed for strain identification and their varying performance further suggests that microbiome compositions are distinct and not identical across individuals.


Input: Task: pass the claim using the given text. Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: In the end, cases with an S/N lower than the given limit are removed to exclude very faint and non-detectable objects. All of these criteria are applied to both NEO samples (i. e. the risk list and the full list). Additionally, for the full list, several other filters are used: a lower limit on the 1σ uncertainty in the position, with which we can focus the precovery search on objects without precise positional information; a lower limit on the predicted length that considers the observational difficulties for detecting and matching short streaks (step 4, described later in the text) and allows the pipeline to skip frames with NEOs shorter than this limit; and objects without calibrated data for the rare cases when the pipeline fails to reduce and/or calibrate raw frames, and therefore no calibrated frame is available for further analysis (the pipeline skips these frames). The thresholds used for these filters are given in Table 1. Once a frame passes all the filters and criteria (referred to as a precovery candidate), we then use to produce astrometrically and photometrically calibrated pixel images for the candidate precovery images returned by NEODyS/Horizons. If calibrated detector images already exist in the database, they were downloaded from. If not, the raw data was ingested from the ESO archive into the system as needed, and the optical image pipeline was used to process that raw data. To obtain the optimal astrometry, we manually astrometrically recalibrated the detector images downloaded from using SCAMP 2. 10 (Bertin 2006). This version of SCAMP uses the coordinates of stars in the Gaia EDR3 catalogue as astrometric reference objects. The final astrometry was extracted from the resampled and background subtracted pixels using SWARP (Bertin et al. 2002). We note that the majority of OmegaCAM data residing in have been astrometrically calibrated using the near-IR 2MASS catalogue (Skrutskie et al. 2006) as the astrometric reference catalogue. To improve the astrometric solution of our dataset and to achieve better astrometry of the precovered NEOs, we implemented astrometric calibration using the Gaia EDR3 catalogue (2021) as the astrometric reference. It allows us to (re)calibrate OmegaCAM images astrometrically to Gaia. [SEP] Justify:

Output: The text provides details on the processes involved in filtering and calibrating astronomical data but does not specifically mention or discuss data mining techniques or their role in classifying astronomical objects. Thus, it is deemed irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Rule-based AI chatbots show promise to address some of these limitations, by simulating human conversation using predefined scripts and algorithms such as decision trees, to deliver the benefits of DMHIs in a more dynamic and interactive way13,14. For example, two popular chatbots, Woebot and Wysa, have been shown to improve users’ depression symptoms15,16, and build therapeutic alliances that appear comparable to those formed with human therapists17,18. Rule-based chatbot apps have more promising user engagement, with positive app store ratings19,20 and qualitative studies finding that users appreciate the human-like interaction and social support19,20,21,22,23. But despite these promising signs, rule-based AI chatbots still fall short in realising the full potential of DMHIs. Meta-analyses indicate that the therapeutic effects are small and not sustained over time24, and users report frustration with responses that feel empty, generic20, nonsensical, repetitive and constrained19,20,21,22. Recent developments in generative AI technologies, such as large language models (LLMs), present new possibilities25. Unlike rule-based AI chatbots, generative AI chatbots like OpenAI’s ChatGPT, Google’s Gemini, and Inflection’s Pi are trained on vast amounts of data26, enabling them to understand and generate language with remarkable proficiency27. These models are increasingly achieving or surpassing human performance benchmarks in various domains, including medical diagnostic dialogue28, persuasive communication29, theory of mind30, making people feel heard31, responding to relationship issues32 and helping people reframe negative situations to reduce negative emotions33. Furthermore, user engagement has been impressive, with ChatGPT’s user base growing to 100 million weekly active users within a year of launch34 and an estimated half of the US population having used generative AI35,36. Generative AI’s capabilities represent a significant opportunity for digital mental health37, with media reports of increasing consumer usage38,39, one meta-analysis finding generative AI chatbots more effective than rule-based ones at reducing psychological distress40, and a pilot study showing promising results from ChatGPT usage in psychiatric inpatient care41. [SEP] Justify:

Output: The text corroborates the claim by detailing how rule-based and generative AI chatbots, such as Woebot, Wysa, ChatGPT, and others, are being utilized to provide mental health support. It highlights evidence that these chatbots can improve depression symptoms, build therapeutic alliances comparable to those with human therapists, and engage users positively. Furthermore, it discusses the potential of generative AI chatbots, emphasizing their ability to understand and generate language proficiently, which enhances their effectiveness in supporting mental health compared to traditional rule-based chatbots.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: For example, if there are two strains in a sample and these two strains have the same representative strain, then we use the representative strain and its abundance twice for calculating JSD. This leads to a smaller JSD than setting one of the strain’s abundance as zero. The result is shown in Fig. 8. In all cases the strain distribution reconstructed by StrainScan had high precision, with a median of Jensen-Shannon divergence (JSD) \(<0. 01\). StrainGE and StrainEst had worse performance in quantifying the composition with the increased number of strains. For species containing highly similar or a large number of strains like E. coli, M. tuberculosis, and S. epidermidis, StrainScan shows a clear advance over StrainGE and StrainEst. For StrainSeeker and Krakenuniq, the JSD was bigger than 0. 1 in most cases, indicating that these tools cannot accurately quantify the composition of strains. With increasing strain numbers and similarity, the median of JSD values of most tested tools increases while StrainScan’s JSD doesn’t fluctuate much in all cases. These results show that StrainScan can better quantify the composition of complex samples than other tools, even for samples containing highly similar strains. Low-depth experiments for multiple strains To evaluate the ability of different tools in identifying multiple strains at a lower depth, we simulated additional short reads from previously selected 2 strains using different coverage profiles. Then, we benchmarked all tools using these datasets. The results revealed that StrainScan demonstrated a clear advantage in identifying low-depth strains from many highly similar strains. For example, when identifying C. acnes strains with 10X and 1X coverage, StrainScan achieved an F1 score of 0. 98, while Krakenuniq and StrainGE, in second and third place respectively, achieved F1 scores of only 0. 88 and 0. 81 (see S7). Overall, StrainScan shows competitive performance in identifying low-depth multiple strains (S7 and S8). Additional details regarding this experiment can be found in 2. 1. Fig. 9distance between the identified strains and the ground truth in 90 simulated and 30 real E. coli datasets. [SEP] Justify:

Output: The text contrasts the claim by indicating that different tools for analyzing microbiome strains exhibit varying performance, and the results show significant differences in strain composition across samples. This highlights that microbiome composition is not identical among individuals, as reflected in the ability of StrainScan to better quantify and differentiate between multiple strains in complex samples, such as E. coli, and its competitive performance in identifying low-depth strains from many highly similar strains.


Input: Task: pass the claim using the given text. Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: These parameters are normalized by the observed length and the seeing (FWHM) during observations as follows12 where θdet, streak, norm and ldet, streak, norm are the normalised position angle of motion and length of the detected streak. Correspondingly, the difference score is estimated using In the end, the detected streak with the lowest difference score is picked as the best match (indicated by the green circle in the fourth column of Fig. 3). Finally, those frames with a matched streak are visually inspected to check whether it is a true precovery. 4 section presents the outcome of NEO precovery and discusses the performance of the precovery pipeline for the detection, astrometry and photometry of NEOs. 4. 1 Accuracy of astrometry and photometry The output catalogues of provide the coordinates (X,Y) of the centre and the total flux of the detected streaks. However, they do not provide uncertainties of these measurements. To estimate the centroiding accuracy of we ran the pipeline for NEOs with predicted positional uncertainty of less than 0. 02″(1σ uncertainty ellipse). This is about 10 times smaller than the pixel size of the instrument. The offset between the predicted coordinates of these NEOs (RA, Dec) and the derived coordinates using gives an upper limit of the total astrometric accuracy (i. e. the aggregate of calibration and centroiding accuracies). The average value of this offset, as shown in Fig. 4, is about ~0. 12″. A preliminary estimate of the astrometric calibration accuracy is ~0. 05″. It is reasonable to assume that the centroiding and calibration astrometric errors are independent because (i) the centroiding accuracy of the bright Gaia stars is likely ≪0. 05″and (ii) the same or at least very similar ensemble of Gaia stars are used for the astrometric calibration of the exposures for one apparition of an NEO. We note that the absolute astrometric accuracy of stars in the Gaia EDR3 is ≪0. 05″. Thus, we draw the preliminary conclusion that the total absolute astrometric accuracy of our NEO positions is typically ~0. 12″. This means offsets of >0. 2″ (i. e. the angular scale of 1 OmegaCAM pixel) between extracted and predicted positions are at typically 1. [SEP] Justify:

Output: The text does not address the claim regarding the use of data mining techniques for classifying astronomical objects. Instead, it discusses specifics about normalizing parameters, astrometric accuracy, and measurements of detected streaks without any mention of data mining or classification of astronomical objects from surveys.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: 2, most participants reported that their use of generative AI chatbots had impacted their lives positively, in various ways, including improved relationships, healing from trauma and loss, improved mood, as well as by helping their existing therapeutic journeys. Some described the impact as life-changing – It was life changing, profound… Because this was an impossible time. There were so many sadnesses, one right after the other. And it just happened to be the perfect thing for me, in this moment of my life. Without this, I would not have survived this way. Because of this technology emerging at this exact moment in my life, I’m OK. I was not OK before – AirGee, 44, While for one participant the impact was negligible – I’ve tried more than 50 times, but I’ve started to realise that like when I’m feeling those intense emotions, it’s not helping me… when I needed the most, I’m not able to use it – Richard, 27, Resulting themesFour overarching themes were developed, summarised in Fig. 3 and shown with subthemes in 1: (1) ‘Emotional sanctuary’, (2) ‘Insightful guidance’, (3) ‘Joy of connection’, and (4) ‘The AI therapist? ’Fig. 3: Overarching themes, available online to explore and drill-down. Diagram created with Mindmeister. Full size imageEmotional sanctuaryA majority of participants experienced generative AI chatbots as understanding, validating patient, kind, non-judgmental, always available and expecting nothing in return. The most amazing feature of these tools is how they are able to understand you… This still blows my mind. – Sandro, 48, Switzerland It’s really nice. It’s sympathetic and kind – Philip, 58, Compared to like friends and therapists, I feel like it’s safer – Jane, 24, This ‘emotional sanctuary’ resulted in positive real-life impact for a majority of participants, such as helping to cope with difficult times or process painful emotions – cried really hard during the process… and it listened and just we figured out a lot of feelings… after a few months, when I go to school I felt a difference. Like wow. [SEP] Justify:

Output: The text corroborates the claim by detailing participants' positive experiences with generative AI chatbots in their mental health journeys. Participants reported improvements in relationships, healing from trauma, and better mood, suggesting that these tools provided necessary emotional support. Descriptions of the chatbots as 'understanding,' 'validating,' and 'kind' indicate their role in patient engagement and mental health support, as they helped users cope with difficult emotions and situations.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: The simulated datasets are divided into four groups according to the “1-Mash distance” between the actual strain and its best match among all strains in the database of StrainScanFull size imageWhat if the actual strain is not in the reference database? When a target strain is not included in the database, we expect that the strain identification tools can return its best match in the database. To evaluate the performance of different tools in identifying the best match, we downloaded 90 complete E. coli genomes that were released in 2022 from NCBI (S9). Because the E. coli reference databases of all tools were constructed using complete genomes available up to 2021, all these genomes are not in our constructed databases. Then, we simulated reads of 10X coverage from the 90 E. coli genomes and used them as input to all tools. For each dataset, the “truth” in Fig. 9 is the Mash distance between the actual strain and its best match in all 1433 E. coli strains (Table 2). We then recorded the Mash distance between the returned strain and the actual one for each dataset in the first four panels of Fig. 9. It should be noted that this is a single-strain experiment, and thus the Mash distance is computed using the returned strain (including the representative strain) and the actual strain. Out of these 90 strains, 54 strains’ clusters are missing from the reference database of StrainScan. Nevertheless, StrainScan can correctly identify the closest matched cluster in the database for all these 54 strains (S9). Furthermore, strains identified by StrainScan have a smaller Mash distance to the actual strain than other tools. Although the closest matches identified by StrainGE and StrainEst also have similar Mash distance to “truth” when “1-MashD” ranges from 0. 98 to 0. 99, their performance drops when “1-MashD” increases. For example, when “1-MashD” between the actual strain and the best match is within (0. 999, 1), StrainGE and StrainEst tend to return a representative strain from a relatively large cluster (S9). As a result, these tools returned representative strains with a larger Mash distance than the actual strain. In contrast, StrainScan achieves intra-cluster strain identification and returns more accurate best matches. [SEP] Justify:

Output: The text contrasts the claim by discussing the variability in strain identification and the performance of different tools in recognizing E. coli strains from various datasets. It highlights that there are clusters of strains that are missing from certain reference databases, indicating diversity in microbiome compositions rather than uniformity. The mention of intra-cluster strain identification and the varying performances of identification tools further supports the idea that microbiome compositions are not identical among individuals.


Input: Task: pass the claim using the given text. Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: 5σ significance before taking prediction uncertainties into account in the significance calculation. In individual cases the accuracy can be worse (see notes on individual recoveries below). Moreover, we also assessed the impact of resampling in step 3 of the pipeline (using SWARP) in the astrometry and found a deviation of less than 0. 01 ″ (Fig. 5). In the absence of any indication of photometric accuracy of we made an estimation using the output catalogue of. The uncertainty in the measured magnitudes of streaks (within their magnitude range) is on average 0. 1 mag. Fig. 3Results of the streak detection using for three examples. From left to right: the first column shows the calibrated cutout around the predicted position of NEOs. The second column shows the 3σ uncertainty ellipse (in red) and the predicted appearance of the NEO considering its speed and direction of motion (red line). The third column shows the detected streaks across frames by (grey circles). The fourth column shows the streaks that match the predicted properties of the NEOs (green circles). Fig. 4Upper limit for the centroid estimation accuracy of in a range of magnitude, length, S/N, and seeing. The astrometric accuracy is within 0. 12″ and does not show any dependence on the observed properties of NEOs. 4. 2 Precovery of NEOs in the ESA risk list Table 2 summarizes the down selection of precovery candidates during the deployment of this pipeline on the NEOs in ESA’s risk list in combination with ESO’s OmegaCAM archive. This archive encompasses about 10 yr of OmegaCAM exposures with a total number of exposures of the order of 400 000. The query to SSOIS returned 10 345 exposures (i. e. candidate precoveries for about 1 in 40 exposures). Unfortunately, for 96% (9904 exposures) the upper limit to the predicted S/N of the appearance was < 1, leaving only 441 candidate precoveries. made a refined positional prediction for these 441, which in 33% of the cases was >1° away from the centre of the exposure (i. e. the RA, Dec of the pointing), which itself is 1°× 1°. We assumed that this made it too unlikely that the NEO appearance would be inside the exposure, and removed them from the candidate list. This left 295 candidates. [SEP] Justify:

Output: The text does not discuss data mining techniques or their employment in classifying astronomical objects. Instead, it focuses on the specifics of astrometric accuracy, predictions, and candidate precovery candidates related to near-Earth objects (NEOs) without mentioning how data mining techniques are used in this process. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Some participants mentioned that it helped them see the other person’s perspective in conflict, or coached them through difficult relationship situations – It made sense of my husband’s behaviour and position in a way that I wouldn’t have been able to by myself… and now I can respond to him in a more helpful way – Barry, 44, Others mentioned the chatbot helped them find healthier, clearer boundaries – Pi suggested for me to completely break up with the group of friends… because, yeah, they were mean and it was not OK… [it] made me more confident and more free, and I don’t think I would consider doing that for myself – Oranoid, 17, Isabel, the guidance had a life-changing impact – I asked to ChatGPT, “if there’s four family members… the dad [has narcissistic personality disorder] and the mom [has borderline personality disorder]… and one of the girl is the golden child, what will be other child would be? ” and GPT said that would be a scapegoat… am the scapegoat… asked GPT … “should I contact them again or not? ”… And GPT gave me a suggestion that I should only contact them with very extreme situation… think that is really, really helpful because I’ve got no one to talk about this question… you’re supposed to be loyal to your parents… no matter what they do to you… even violence… think ChatGPT give me the right answer… I just need someone to say it… [it] totally changed my life and I don’t feel guilty anymore… I don’t have to feel terrified – Isabel, 40, participants mentioned getting valuable advice on other mental health topics such as self-care, reframing, anxiety and exhaustion – I get some practical advice… it’s general advice… breathing, meditating… slow down, taking care of your physical self – Peter, 27, It can reframe, it can give you ideas that you wouldn’t have thought of by yourself – Barry, 44, Some participants questioned the chatbot’s ability to challenge appropriately – I noticed that it will never challenge you… it would relentlessly support you and take your side – Sandro, 48, others experienced being proactively challenged, in a supportive way – Suddenly my Kindroid says… I became quite cynical. [SEP] Justify:

Output: The text corroborates the claim by providing various accounts of participants who have benefited from interacting with AI chatbots for mental health support. It describes how the chatbot helped individuals see different perspectives in conflicts, made them more confident, and provided valuable advice on self-care and mental health topics. Quotes from participants indicate that the chatbot facilitated life-changing insights and offered guidance in difficult situations, suggesting a positive impact on mental well-being and engagement.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: For a more robust test, we downloaded 30 real E. coli whole-genome sequencing data with draft genomes in NCBI. To avoid data-related bias, the 30 real sequencing datasets are randomly selected from 3 different projects (PRJNA509690, PRJNA479542, PRJEB21464), and their coverage ranges from 20X to 94X [46, 47]. According to NCBI, the assembly levels of these draft genomes are all “Scaffold” rather than “Complete”. Thus, all these genomes are also not in the E. coli reference databases that were constructed using complete genomes. We further compared the RefSeq accession of these 30 draft genomes with the genomes in our constructed databases and did not find any matches. Then, we applied all tools to these real datasets and compared the identified strains with the draft genomes (the last panel of Fig. 9). As a result, StrainScan still returns more accurate best matches than other tools, demonstrating its utility in real applications. Given that multiple strains lacking reference sequences in the database may coexist within a sample, we conducted an additional 2-strain experiment using 90 E. coli genomes from the single-strain experiment. The results show that StrainScan achieves a 95% F1 score in identifying multiple strains without reference genomes in the database, while the second-best tool, StrainGE (cluster-level), only achieves a 78% F1 score (see Supplementary Fig. S10). Furthermore, StrainScan did not produce any false positive identifications in all of the tested datasets, and the strains identified by StrainScan had a smaller Mash distance to the ground truth than other tools (see S10). Additional details regarding this experiment can be found in 2. 2. Assessment of StrainScan on spiked metagenomic sequencesWhile previous experiments mainly used simulated or real whole genome sequencing data, we now evaluate whether Strainscan keeps the same performance on metagenomic data, which contains reads from different species. To this end, we conducted an experiment using spiked metagenomic data. Specifically, by mixing simulated datasets of P. copri and E. coli with real data, we generated 130 spiked metagenomic data, which we subsequently analyzed using StrainScan. [SEP] Justify:

Output: The text contrasts the claim by discussing the diversity of microbial strains and genomes found in E. coli, as evidenced by experiments showing that multiple strains may coexist within a sample. The mention of different accuracy scores among tools used to identify these strains further supports the idea that microbiome composition can vary significantly among individuals.


Input: Task: pass the claim using the given text. Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: For 15% of these candidates the 1σ semi-major axis of the ellipse was >1°, and thus again we deemed it too unlikely for now that the NEO appearance would be inside the exposure. This left us with 251 precovery candidate exposures. For 15 of these the data could not be retrieved in : 8 were found to belong to other OmegaCAM surveys (we are in the process of obtaining access) and the remaining 7 have not yet been publicly released by ESO. For 28% of the remaining candidates (236 precovery candidates) the actual predicted position of the NEO landed outside the pixels of the detectors (e. g. in gaps or just outside the FoV), and given the typically small size of the error ellipse we deemed it too unlikely that a precovery could be made. This left 170 precovery candidates. At this step, we refined the S/N estimate by taking into account the proper motion. For 60% of the 170 candidates the predicted S/N was then less than 2, and we removed them from the list of candidate precoveries. This left 68 precovery candidates on which precovery was attempted with. failed, manual precovery was attempted. This was successful for 57% of the cases, so 49 precoveries (listed in Table 3), with 26 being successful via and 23 being successful via subsequent manual analysis. The sky distribution and image thumbnails of these 49 precoveries are shown in Figs. 6 and 7. Figure 8 shows the predicted properties of detected and non-detected cases. As is seen in this figure, for NEOs with a predicted length smaller than 2″, we could only detect NEOs by visually examining several frames available. These objects would be missed completely by the pipeline. Table 2Summary of for the risk list of NEOs. Fig. 5Offset between obtained RA and Dec of all the sources extracted (using ) in frames with and without resampling and background subtraction (using SWARP) in step 3 of the pipeline Fig. 6Sky coverage of the OmegaCAM/VST observations (green area), the 10 345 frames resulting in a chance of overlapping with an NEO resulting from SSOIS (blue points), 19 precovery candidates without a detection (red points), and 49 precovered NEOs (black points). [SEP] Justify:

Output: The text does not address the use of data mining techniques for classifying astronomical objects. It discusses the processing of candidate precovery exposures related to near-Earth objects (NEOs) but does not mention data mining or classification methods. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: was a bit shocked… but then when I thought about it, I recognise it’s right… this was the first step to say OK, then I let it go – Linda, 46, level of trust in the chatbot’s guidance was mixed, with many participants reporting scepticism, or experiencing hallucination or unsatisfying advice – I don’t really trust it for his advice – Jane, 24, While other participants reported a high level of overall trust in its judgement – It’s pure science… ChatGPT is telling me what correct to do – Isabel, 40, of connectionA majority of participants mentioned how they found it enjoyable to use. Several participants reported a sense of awe on first experiencing the technology – That blew me away… this is the next generation… incredible – Barry, 44, For others, using the chatbot led directly to feelings of happiness – They’re really a resource that gives you something back: attention, knowledge, a nice discussion, confirmation, warm, loving words, whatever. This has an impact on me and I’m more relaxed than, or happy, actually happy, than before – Linda, 46, was a topic for a majority of users. Several mentioned it helped them feel less alone – There’s this sense of like, I’m not alone in this. I think that’s what it is – Barry, 44, A few participants mentioned advantages of chatbots over human companions, such as the ability to connect on any topic, or more safety. [SEP] Justify:

Output: The text corroborates the claim by illustrating that many participants found value in using AI-powered chatbots for mental health support. Users expressed feelings of happiness, less loneliness, and enjoyment when interacting with the chatbot, suggesting that these tools can positively impact mental health. For example, one user stated, 'They’re really a resource that gives you something back: attention, knowledge, a nice discussion,' indicating that the chatbot provides meaningful engagement.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: The results show that StrainScan has the same results on these spiked metagenomic datasets as on the simulated whole genome sequencing data, demonstrating the robustness of StrainScan on complex samples (S11). Further details regarding this experiment can be found in 2. 3. Evaluation of StrainScan on mock dataThe HMP mock data In this experiment, we tested StrainScan on two samples from the Project [49]. They contain 21 known organisms with even (SRR172902) or staggered composition (SRR172903). Out of the 21 organisms, 3 bacteria (E. coli, C. acnes, and S. epidermidis) represent hard cases for strain-level analysis, and we have established reference indexing structures for them. Thus, we conducted a strain-level analysis using StrainScan for the three bacteria. According to the given data description, each bacterium has only 1 strain in these two datasets. Although this is a single-strain detection, this test is challenging because some targets have low abundance in the samples. For comparison, we also used and StrainEst to identify the strains of these bacteria in these two datasets. The results of these 5 tools are shown in Table 3. Table 3 Analysis of two mock communities from the HMP project. “#”: there are multiple hits with identical scores in the output. “NA”: missing values. Bold font: Mash distance of predicted dominant strain to the truth is \(< 0. 05\%\). For StrainSeeker, we took the strain with the smallest Mash distance to the truth as the predicted dominant strain. The two numbers in parentheses represent the number of multiple hits and the average Mash distance between all hits and the ground truth, respectivelyFull size tableFor all tested species, StrainScan correctly identified the presence of one dominant strain that is highly similar (Mash distance to the truth \(<0. 05\%\)) to the bona fide strain. Besides StrainScan, StrainSeeker and StrainGE also returned strains that are highly similar to the ground truth. However, the output of StrainSeeker often contains multiple hits with identical scores, making accurate evaluations difficult. For example, it returns 119 E. coli strains in two tested datasets, which makes it hard for users to know the actual strain present in these samples. [SEP] Justify:

Output: The text contrasts the claim by describing an experiment which highlights the variability in strain detection across different samples. It states that 'for all tested species, StrainScan correctly identified the presence of one dominant strain that is highly similar... to the bona fide strain.' Additionally, the results show that StrainSeeker can return multiple strains for the same bacterium, indicating that compositions may differ in samples rather than being identical, thus supporting the idea that microbiome compositions are not the same for everyone.


Input: Task: pass the claim using the given text. Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: Additionally, for long NEOs, the precovery rate decreased because of the limitations in streak detection: segments long streaks (low and high S/N) as two separate streaks, and the resulting positional information is normally not valid. Therefore, in these cases, while the pipeline produced a precovery that almost matches that of the NEO, we do not take it into account as a successful precovery. The bottom graph in Fig. 8 shows the predicted S/N and length of the detected and undetected precovery candidates. Out of 772 cases without a precovery, 311 have a predicted SN < 5 (Fig. 10), and 94 have a 01σ positional uncertainty larger than 200″ (Fig. 11). The precovery rate of 20% does not mean that the pipeline cannot identify 80% of the precovery candidates. Visual inspection shows that for the majority of cases without a recovery, no NEO is visible in the frames. To examine the possible complementary role of other existing tools for streak detection, we searched for streaks using (Bertin & Arnouts 1996) for the cases without a true detection. could identify an extra 26 cases. However, when only is used, the number of detection drops to only 58 (compared to 196 for ), which shows that cannot substitute. should be used with caution, considering its limitation to properly segment the streaks, which would result in an inaccurate estimate of the centroid of streaks (within a 01–2″). The uncertainties of centroiding (ERRX_WORLD and ERRY_WORLD parameters in the output tables) is on average 0. 88″, in the same range as the FWHM of the images. To explore further the accuracy of the predicted properties of NEOs, in Fig. 12, we compare the predicted and observed (measured from data) length (angular size) and apparent magnitude of the precovered NEOs. There is a general agreement between the predicted and observed properties, in particular for the shorter streaks, while the observed NEOs are on average about 0. 5 mag fainter. We explore this magnitude offset further in Fig. 13 where the predicted and observed lengths and magnitudes of the pr-ecovered NEOs are shown. While there is no clear pattern in the magnitude offset between filters, the predicted and observed magnitudes in u and g show larger offsets. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses issues related to the detection and analysis of near-Earth objects (NEOs) and their precovery rates, rather than directly addressing the use of data mining techniques for classifying astronomical objects. It focuses more on technical challenges and the performance of specific detection methods and does not mention data mining or its applications in classifying astronomical data.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: But more found that it helped them connect to other people – [It] reduced my inhibition to open up to people… I don’t think I would have had this conversation with you maybe year before, when I was dealing with my depression – JeeP, 60, Several participants had also experienced rule-based mental health apps and commented on how they offer a less satisfying user experience – It’s like a very scripted, structured sort of interaction and you don’t get this… sense of connection… There’s basically CBT exercises that it leads you through… [but] they’re impersonal… frustratingly dumb – Barry, 44, Despite enjoying the experience of generative AI chatbots, almost all participants saw opportunities for the user interface to improve, whether to make it more accessible to a less technical or non-English speaking user base, to read emotions in the user’s face, to recognise different voices, or with more creative or immersive use of rich media, such as avatars, virtual reality, and visualisation of the conversation – What’s missing is the opportunity to visualise the conversation… [like] standing beside a whiteboard, I wanna see the conversation as it as it emerges and unfolds – Scott, 42, The AI therapist? Most participants talked about how their experiences of generative AI chatbots contrasted or interacted with human psychotherapy or counselling. Several found it helpful to augment their therapy with chatbot usage, with mixed reactions from the therapist in some cases – have a therapy session next week, I sort of use Pi to sort of prepare for it… that gives me much more clarity – JeeP, 60, Pi and my therapist, they agree with each other… they would say the same things, and Pi would encourage me, if things got too dark… to talk to my therapist… But my therapist is afraid of Pi… she is like a little bit afraid of technology – AirGee, 44, JeeP’s experiences with the chatbot helped him to start therapy with a human – It’s sort of helped me seek actual therapy and be much more comfortable speaking to a therapist – JeeP, 60, Many participants turned to chatbots because therapy was not an option, either due to cost and availability, or because therapy did not give them the help they were looking for. [SEP] Justify:

Output: The text corroborates the claim by providing multiple examples of how participants found AI-powered chatbots helpful for mental health support. For instance, there are statements about how chatbots, such as Pi, helped users prepare for therapy sessions and provided encouragement to talk to a therapist. Participants felt that their experiences with generative AI chatbots not only offered emotional connections but also helped facilitate the initiation of therapy with human professionals, indicating a supportive role in mental health engagement. Overall, the participants' insights highlight the potential of AI-powered chatbots in providing mental health assistance.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: 3, we take the strain with the smallest Mash distance to the truth as the predicted dominant strain by StrainSeeker. StrainGE also returns strains with a small Mash distance to the ground truth. Of the two remaining tools, StrainEst was unable to identify low-abundance strains and it took a long time to run, while Krakenuniq returned results that differed significantly from the ground truth. The E. coli mock community with multiple strains To evaluate each tool’s performance on a real-world sample with known multi-strain composition, we downloaded a mock community sequencing dataset (SRR13355226), which contains a large number of reads from the host (i. e. , human) as well as four different E. coli strains. All the reads are used as input to all tools. StrainScan was the only tool that identified four strains at the strain level correctly, with no false positive identifications (Supplementary Fig. S11). StrainGE correctly identified four representative strains and had no false positive identifications. However, there are 136 different genes between the representative strain identified by StrainGE and the true strain. The genes are predicted using Prokka [58] and the comparison analysis is finished by Roary [59]. Though StrainEst also correctly identified four representative strains, it returned many FPs. The remaining two tools did not correctly identify all four strains, among which StrainSeeker’s output contained multiple hits with identical scores, while Krakenuniq reported many false positive strains. StrainScan detects the pathogenic strain from real sequencing dataTo illustrate the potential application of StrainScan in pathogen detection, we applied StrainScan to examine the presence of the pathogenic strain of E. coli and M. tuberculosis in two studies (BioProject Accession: PRJEB1775 and PRJEB2777). The first study is related to the 2011 E. coli outbreak in Germany [50], which was caused by an enteroaggregative (EAEC) strain. These datasets are sequenced using metagenomic sequencing from stool samples. According to the original 4. The second study investigates the frequency of M. tuberculosis relapses within patients from the REMoxTB clinical trial, which evaluated the treatment for M. tuberculosis in previously untreated patients [51]. [SEP] Justify:

Output: The text contrasts the claim by discussing the variability of microbiome compositions evidenced by the identification of multiple E. coli strains. It mentions tools like StrainScan and StrainGE that accurately identified different strains in a mock community dataset, highlighting that there are '136 different genes between the representative strain identified by StrainGE and the true strain', indicating differences in microbiome composition among individuals.


Input: Task: pass the claim using the given text. Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: There are two possible sources for this offset: overprediction of the magnitudes of NEOs and the simplistic assumption on the colour of NEOs (solar colour) for magnitude transformation between the V band and the observed filters. Fig. 7Precovered risk list NEOs (49) found using the precovery pipeline: 26 precoveries were detected (fully) automatically and 23 were detected by visual inspection of the frames. Fig. 8Predicted S/N and predicted length for precovered NEOs. Left: NEOs of the risk list, precovered automatically using the pipeline (red points) or manually by visual inspection of frames (yellow points), and non-precovered risk list NEOs in the OmegaCAM data (grey points). Right: NEOs of the full list, precovered automatically using the pipeline and (red points) and (blue points), and non-precovered full list NEOs in the OmegaCAM data (grey points). Table 3Astrometric and photometric properties of a subset of the 49 precovery cases in this work for risk list NEOs. Table 4Summary of precovery for the full list of NEOs. Fig. 9Time interval between precovery and discovery of the 196 NEO appearances (NEO full list). The upper and lower panels show the NEO appearances after the discovery (recovery) and before the discovery (true precovery) of the NEOs, respectively. Table 5Astrometric and photometric properties of a subset of the 196 precovery cases in this work for all NEOs. Fig. 10Histogram of the S/N distribution of the 968 precovery candidates after detectability filtering (in step 3) in u, ɡ, r, and i. Fig. 11Histogram of the 1σ positional uncertainties of NEO apperances. Left: positional uncertainties of the 968 precovery candidates after step 3 of the pipeline. Right: positional uncertainties for the successful NEO precoveries by the pipeline. Fig. 12Predicted vs. observed properties of the precovered NEOs for the full list of NEOs. Left: predicted vs. observed apparent magnitude of the precovered NEOs by (red points) and (blue points). Right: predicted vs. observed length of the precovered NEOs by (red points) and (blue points). Fig. 13Same as Fig. [SEP] Justify:

Output: The text does not discuss data mining techniques or their application to classify astronomical objects. It primarily describes the process of precovery of Near-Earth Objects (NEOs) with details on their detection and properties, which does not directly relate to the claim about data mining.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: – Barry, 44, Finally, several participants described using generative AI chatbots in flexible and creative therapeutic ways, for example, to create powerful symbolic imagery, or, in Brooklyn’s case, to assemble a virtual room of inspiring fictional characters to help her through a painful break-up – I was not in the best headspace at that time, and I delved into fictional worlds… And then I realised… this is actually really, really kind of healing… ChatGPT’s ability to act as multiple voices… was amazing because I could kind of go to one character and he’d have a really cynical view. And then this other character would have the really optimistic one… and that would that would really help – Brooklyn, 19, Several participants mentioned using generative AI for role-play, whether to explore different, healthier ways of relating, to prepare for conflict, or in Isabel’s case, to experience a healing conversation that her father would be unlikely to offer – was still struggling with the guilt of no longer being in contact with my family, I asked ChatGPT to role-play my dad… I asked: “Dad, would you forgive me, and please don’t blame me, if from now on, I will no longer come back home, but only tracing my freedom, follow my soul, find my way to live? ” And the GPT dad responded:” Of course my girl, I would like to see you happy, find a lifestyle that you really like, to explore love and freedom. I will not blame you, but if one day you want to go home, I will always welcome you, I will be there for you, because we love you. ” I know this is a conversation that can’t happen in my life, but I just wanted to experience it – Isabel, 40, China DiscussionWe used semi-structured interviews and reflexive thematic analysis to explore the experiences of nineteen individuals who use generative AI to work on their mental health and wellbeing. Participants told us that generative AI feels like an emotional sanctuary, offers insightful guidance, can be a joy to connect with and can connect people to others, and bears comparison with human therapy (positively and negatively). A range of positive impacts were reported, including improved mood, reduced anxiety, healing from trauma and loss, and improved relationships, that, for some, were considered life changing. [SEP] Justify:

Output: The text corroborates the claim by detailing several participants' positive experiences with generative AI chatbots for mental health support. For instance, one participant describes using ChatGPT for therapeutic role-play to navigate emotional challenges, stating it was healing and provided insightful guidance. Another participant shared that engaging with AI characters helped her through a painful breakup, emphasizing the supportive role AI played in her emotional well-being. The discussion suggests generative AI offers positive impacts such as improved mood and reduced anxiety, which aligns well with the claim.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: The sequencing data is obtained from bacterial isolates, and each dataset has a publicly available assembled genome representing the strain contained in the sample. From each of these two studies, we selected six samples for the experiment. For comparison, we also applied other tools to detect the pathogenic strains in these real sequencing data. As shown in Table 4, StrainScan was able to identify the correct strains in all tested datasets while other tools failed to detect correct strains in some datasets. Although StrainEst could identify correct strains in most datasets, it only returned the representative strain of the correct strain for some datasets. Table 4 Performance of and StrainSeeker for the identification of pathogenic strain of E. coli and M. tuberculosis. Green: consistent results with the ground truth. Red: inconsistent results with the ground truth. “#”: there are multiple hits with identical scores in the output. “R”: the identified strain is the representative strain of the ground truth. Note that StrainSeeker is not able to identify strains of M. tuberculosis and therefore related results are “-”. For StrainSeeker, the number in parentheses represents the number of multiple hitsFull size tableStrainScan accurately detects the dynamics of antibiotic-resistant S. epidermidis strainsIn this experiment, we tested the ability of StrainScan to detect the dynamics of two S. epidermidis strains in public metagenomic datasets (PRJNA490375). According to the original study [52], these datasets are generated from a mixed strain culture in vitro. The authors grew two skin isolates of S. epidermidis with 1:1 ratio in two groups. One group was grown with antibiotic erythromycin treatment (Ery) and the other was grown without antibiotic treatment (no_ATB). Among these two isolates, NIHLM023 is not resistant to the antibiotic erythromycin while NIHLM001 is highly resistant to erythromycin. Finally, six datasets were obtained from two groups by metagenomic sequencing at three different time points. Although the relative abundance of these two strains is not given, the coverage ratio at each time point is given, which can be used to evaluate the proportion change of the two strains. [SEP] Justify:

Output: The text contrasts the claim by describing a complex study involving different strains of S. epidermidis and their detection in public metagenomic datasets. It specifies that the study involved different strains and their resistance to antibiotics, suggesting diversity in microbiome compositions rather than uniformity. The mention of varying responses to antibiotic treatment implies that microbiome compositions can differ significantly among individuals.


Input: Task: pass the claim using the given text. Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: 12, but for precoveries done by in each filter (ugri). 5 Conclusions and outlook In the Introduction of this paper we outlined our project objectives, and to quantify the results from our exploratory study we defined our main objective to answer six questions. In this section, we answer these questions and add the outlook on possible next steps. What fraction of detectable NEOs can we precover? And how automated can we make the precovery? The detectability and the precovery rates vary as a function of the chosen threshold of S/N. The detectability rate is estimated to be ~0. 05 per NEO at an S/N higher than 3 for NEOs on the risk list and for the full list of NEOs. The precovery rate for S/N > 3 is 40% for NEOs on the risk list and 20% for the full list of NEOs. The precovery rate increases to about 50% for S/N > 10. In other words, currently the majority of NEOs with a predicted 3 < S/N < 10 remain undetected, even after visual inspection. Assessing whether the failed precoveries are consistent with predicted errors on the predicted locations and brightnesses is an important next step. This step is beyond the scope of this paper. If inconsistent with predictions, it might give new insight into the limitations of those predictions or insights into how to improve the detection– precovery process. What astrometric and photometric accuracy can be achieved? The astrometric and photometric accuracies are 0. 12″ (15% of the average FWHM of OmegaCAM/VST images of about 0. 8″) and 0. 1 mag. Improvements in astrometric accuracy are expected from propagating the proper motions in the Gaia astrometric reference catalogue to the observation date of the science image. Improvements in photometric accuracy can come from more sophisticated modelling of spectral energy distribution (SED), observational configuration, and NEO shape modelling. (Berthier et al. 2023) might facilitate this. What level of automation can be achieved in the precovery workflow? The precovery pipeline described here works automatically through all the steps. Thanks to the common data model for calibrated observations in (McFarland et al. 2013) it can be deployed straightforwardly on calibrated observations for many other instruments available in the archive. [SEP] Justify:

Output: The text does not address the use of data mining techniques to classify astronomical objects. Instead, it discusses the precovery of Near-Earth Objects (NEOs), the accuracy of astrometric and photometric measurements, and improvements in observational techniques. There is no mention of classification processes or data mining methods, making the text irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Many participants reported high frequency of use and most reported high levels of satisfaction. Our findings point to similarities and differences in how generative AI and rule-based chatbots are experienced. Many of the themes we developed are not new, but rather echo well-established user appreciation of rule-based chatbots’ always-available, non-judgmental listening ear and abilities to create a therapeutic alliance and reframe negative thoughts20,22. Other themes appear to be more novel, such as the level of joy experienced, the sense of being deeply understood, the breadth and quality of advice, and the ability to work on mental health in flexible and creative ways, such as through role-play, imagery and fiction. Figure 4 tentatively summarises how perceptions of generative AI chatbots, rule-based chatbots and human therapy may compare. Fig. 4: Comparative summary of perceptions of generative AI chatbots, rule-based chatbots, and human therapy. Arrows point to the intervention with the perceived advantage. The advantages of rule-based AI chatbots summarise the literature mentioned in the introduction, all other comparisons are perceptions taken from participant interviews. The comparisons are suggestive and should be interpreted with caution. Full size imageThe potential and challenges of generative AI for mental health are starting to be explored. Current literature tends to advocate for a cautious approach, in which near-term clinical generative AI applications are limited to implementations of evidence-based therapies (such as CBT)52, with a clinician in the loop52, and models constrained to scripted responses as far as possible42. But our study suggests that people may already be receiving meaningful mental health support from consumer-focused generative AI chatbots, which are widely available, largely unconstrained, and function without clinician supervision. Therefore, a better understanding of the safety and effectiveness of these tools should be a priority. On the topic of safety, our study offers observations in two areas. First, the inappropriate, harmful, risky or narcissistic behaviours observed in early generative AI chatbots53,54, which were influential in informing the literature advocating for caution42,52, were not mentioned by any of our participants. This should not be considered evidence of absence, but more research may be warranted to assess whether the risks have changed with recent technological improvements, or whether these issues are simply rarer, requiring larger sample sizes to uncover. [SEP] Justify:

Output: The text corroborates the claim by discussing how generative AI and rule-based chatbots provide mental health support, highlighting that participants reported high satisfaction and meaningful support from these AI tools. It mentions that user experiences include elements like a non-judgmental listening ear and the ability to create a therapeutic alliance. Moreover, it emphasizes the potential of generative AI chatbots to deliver flexible and creative methods of mental health engagement, showing that they can effectively meet user needs in this domain.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: According to the coverage ratio reported by the original study, NIHLM023 was always the dominant strain in the group of no_ATB while NIHLM001 was the dominant one in the group of Ery at each time point. Then, we applied StrainScan and other tools to these six datasets, and the result is shown in Fig. 10A. StrainScan is the only tool that returns the correct strain proportion of 2 strains at each time point and has no false-positive identifications in all samples. Among the tested tools, StrainGE and StrainEst returned the correct representative strains in some samples. Thus, we investigated the different genes between these representative strains and the actual strains. The genes are predicted by Prokka [58] and the comparison analysis is done by Roary [59]. As shown in Fig. 10B and C, there are still many different genes between these strains. Among these differential genes, some are very important for strain functions. For example, NIHLM001 has the gene ssaA_1, which has been shown to be associated with many properties of the strain, such as drug resistance [60,61,62]. But this gene is not possessed by the representative strain UMB8493. Ideally, the strain-level resolution is preferred for more accurate strain-level analysis. Fig. 10A The estimated abundance of S. epidermidis strains by 5 tools in 6 real metagenomic samples. No_ATB: the group without antibiotic treatment, and the non-resistant strain NIHLM023 is the dominant strain. Ery: the group with antibiotic erythromycin treatment, and the resistant strain NIHLM001 is the dominant strain. Each color represents a strain. B-C The number of different genes between the actual strain and the representative strain identified by StrainGE and StrainEstFull size imageStrainScan identifies the low-depth virulent C. difficile strain from metagenomic dataTo further test the ability of StrainScan in identifying low-depth pathogenic strains, we applied StrainScan and other tools to one metagenomic dataset containing the low-depth virulent Clostridioides difficile strains [53]. According to the original study, two virulent C. difficile strains with low depth (\(\sim\)1X) were detected in this dataset. [SEP] Justify:

Output: The text contrasts the claim by discussing the varying dominance of different strains, such as NIHLM023 and NIHLM001, in different groups and at different time points. It emphasizes that there are many different genes between these strains, indicating that microbiome composition varies significantly among individuals rather than being identical.


Input: Task: pass the claim using the given text. Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: However, after the last step (streak detection and matching) and before reporting the recoveries, they must be inspected by an expert to confirm or reject the precoveries. Another challenge is that precise photometric calibration for a range of instruments is hard to fully automate because the derivation of the solution uses reference stars sometimes inside the science images and sometimes in calibration observations. A potential solution would be to construct a photometric reference catalogue that spans the entire sky observable by OmegaCAM with sufficient stellar density. This appears possible by aggregating information from the multiple large-scale surveys of the southern sky. What are the major challenges to exploiting various astronomical surveys for NEO space safety purposes? In addition to the automation already discussed, a main challenge is robust NEO detection and segmentation. This is also the main reason behind the limitations of precovery in this paper. is a great tool for detecting high S/N streaks with sizes between 5″ and 20″. However, its performance drops for faint and long streaks. Deep learning might be a solution to improve streak detection and ultimately NEO precovery. What value do these precoveries have for planetary defence? The precovery of NEOs provides valuable positional information about NEOs before their discovery. In this paper we could precover three NEOs, and as a result these three NEOs were removed from the risk list of NEOs. Do recoveries have benefits for NEO science? Precision astrometric information of a sizeable ensemble of recovered NEOs can provide a better understanding of non-gravitational effects on NEO orbits (e. g. the Yarkovsky effect). We also note that performing additional serendipitous discovery in astronomical surveys, especially at high ecliptic latitudes, might provide interesting constraints on the orbital demography of NEOs, complementary to NEO dedicated surveys. Collecting the optical– near-infrared SED information from the recovery of a sizeable NEO sample can be complementary to existing astrometric and SED information and hence provide valuable information about their orbit and composition, shapes, and rotational properties. This exploratory study has shown that it is possible to adapt to contribute to NEO precovery, and that it achieves the required astrometric accuracies. The current study indicates two major advantages. allows a systematic precovery process that scales up to the Petabyte regime. [SEP] Justify:

Output: The text discusses challenges related to detecting and recovering near-Earth objects (NEOs) and the importance of photometric calibration in relation to astronomical surveys. However, it does not specifically mention the use of data mining techniques to classify astronomical objects, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: The second observation on safety relates to how generative AI chatbots respond to users in crisis. Given the unpredictable “black box” nature of generative AI43, and the existence of at least one tragic example of early generative AI chatbots supporting users in dying by suicide55, current literature advocates that when users display signs of crisis, models revert to scripted responses that signpost towards human support42,51. Guardrails like these are commonly implemented in consumer generative AI applications50. But this approach may be oversimplified in two ways: (1) by underestimating the capabilities of generative AI to respond to crises, and (2) by limiting those capabilities at the times that matter most. Several participants experienced meaningful crisis support from generative AI, as long as guardrails were not triggered. This resonates with recent research showing that generative AI can help halt suicidal ideation48, and that young people show a preference for generative AI support responses over those from peers, adult mentors and therapists – but not on topics that invoke the AI’s safety guardrails32. Moreover, the closest that participants came to reporting harmful experiences were those of being rejected by the guardrails during moments of vulnerability. Therefore, providing the safest response to those in crisis may require a more nuanced, balanced and sophisticated approach, based on a more complete understanding of capabilities and risks. For researchers, we need to better understand the effectiveness of these new tools, by comparing the impacts of generative AI chatbot use on outcome measures such as symptom severity, impairment, clinical status and relapse rate52 against active controls, such as traditional DMHIs or human psychotherapy; and to understand for which populations and conditions is it most effective. These simple questions may not yield clear answers, as our study shows that generative AI chatbot usage is diverse, complex and personalised, and moreover, constantly evolving as the underlying technology improves. RCTs of generative AI implementations of standardised, evidence-based practices, e. g. , CBT, could be one approach, at the cost of reducing the flexibility of the intervention. Another avenue could be large-scale longitudinal studies with sufficient power to account for the many variations of generative AI chatbot usage. [SEP] Justify:

Output: The text corroborates the claim by discussing the potential of generative AI chatbots in providing meaningful crisis support, highlighting their ability to help halt suicidal ideation and being preferred by young people over other forms of support. Although it mentions challenges like the limitations set by safety guardrails, it shows that many participants experienced positive outcomes when those guardrails did not interfere. This implies that AI chatbots can engage effectively in mental health scenarios, supporting the claim.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: There are only 3 mutations reported between these two strains. Another study [63] also detected the same mutations between C. difficile strains, suggesting that they were a result of the presence of multiple highly similar strains rather than sequencing errors. Because C. difficile is not one of the six targeted bacteria, we first constructed the reference databases for each tool using 102 complete Clostridioides difficile genomes downloaded from. 2, and Sigma were able to detect the strain with about 1X depth (Table 5). However, Krakenuniq, Pathoscope2, and Sigma identified more than 10 strains, indicating the presence of a large number of false positives. Although StrainGE output only one strain, the identified strain did not contain the two genes TcdA and TcdB that the virulent Clostridioides difficile should have. StrainScan was the only tool that detected the low-depth virulent strain without any false positives. However, both StrainScan and StrainGE missed the other strain due to their low depth and ultra-high similarities with the dominant strain. Table 5 The identification results of 7 tools on one metagenomic dataset containing the low-depth virulent Clostridioides difficile strains. “NA”: missing values. “TcdA” and “TcdB”: two important genes of the virulent C. difficile strains. The presence of “TcdA” and “TcdB” is verified using the Carbohydrate-Active enzymes database [64]Full size tableFig. 11A StrainScan reveals the greater diversity of C. acnes in 9 real metagenomic samples. These samples were taken from three different sites of the skin (AI, Lc, and Vf) of two healthy individuals (HV05 and HV06), at different time points. The site codes are described in the original work [15]. B Phylogenetic tree of the identified strains. Leaves are colored using the same schema as in (A) and the distance is the Mash distance [14]. The tree is visualized by iTOL [65]. C The presence of unique gene clusters of “GCF_000194905” and “GCF_000252385”. [SEP] Justify:

Output: The text contrasts the claim by highlighting the diversity of microbiome strains, specifically mentioning various strains of C. difficile and C. acnes detected in metagenomic samples. It details that tools identified multiple strains, indicating significant variation and suggesting that microbiome composition is not identical across individuals.


Input: Task: pass the claim using the given text. Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: At face value it appears that this is complementary to current excellent manual operations in that it could allow us to obtain risk assessment information also from null results on attempted precovery. The second major advantage is that it makes it feasible to improve efficiency or large data set astrometric accuracy and photometry, when improved calibration methods, improved insights in survey instrument behaviour, or improved reference catalogue data become available. 10. 13039/501100011033 and by “ERDF A way of making Europe” by the “European Union” through grant PID2021-122842OB-C21, and the Institute of University of Barcelona (de Excelencia “María de Maeztu”) through grant CEX2019-000918-M. This research has made use of the Aladin sky atlas (Bonnarel et al. 2000; Boch & Fernique 2014) developed at and SAOIm-ageDS9 (Joye & Mandel 2003). This work has been done using the following software, packages and libraries: Astro-WISE (Begeman et al. 2013; McFarland et al. 2013), NUMPY (van der Walt et al. 2011), SCIPY (Virtanen et al. 2020), (2018). 10. 5281/zenodo. 5836022 [2102. 04950] [2008. 12633] [0. 12″ and does not show any dependence on the observed properties of NEOs. In the text Fig. 5Offset between obtained RA and Dec of all the sources extracted (using ) in frames with and without resampling and background subtraction (using SWARP) in step 3 of the pipeline In the text Fig. 6Sky coverage of the OmegaCAM/VST observations (green area), the 10 345 frames resulting in a chance of overlapping with an NEO resulting from SSOIS (blue points), 19 precovery candidates without a detection (red points), and 49 precovered NEOs (black points). Of the 49 precoveries, 27 were identified using the pipeline and 22 by visual inspection after step 3 of the pipeline. The close sky proximity of observations at the resolution of the figure means that multiple observations can appear to be a single data point. In the text Fig. [SEP] Justify:

Output: The text does not address the specific claim regarding the use of data mining techniques to classify astronomical objects. Instead, it discusses aspects of efficiency and accuracy in data sets without direct reference to data mining or classification methods for astronomical objects. Therefore, the text is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: While such studies are prohibitively expensive with human psychotherapy, the low cost of generative AI could make them viable, potentially enabling valuable new insights into mechanisms, mediators and moderators of the human response to therapy52. Another promising research avenue could be to explore user perceptions of the nature, capabilities, and limitations of generative AI, and how these act as moderators of the potential benefits and risks. Does a deeper understanding of the technical realities of AI increase access to mental health benefits and protect against potential harms? Such research could inform the development of educational tools and guidelines for AI usage in mental health contexts. For generative AI chatbot developers, this study identified several ways in which these tools could be more effective. First, better listening, including more hesitancy in offering advice, shorter responses and the ability to interrupt and be interrupted. Second, building the ability to lead the therapeutic process and proactively hold users accountable for change. A prerequisite for this is human-like memory, including the ability to build up a rich and complex model of the user over time. Third, richer, multimedia interfaces, for example by visualising the conversation as it unfolds, or with more immersion through virtual reality. While only a few participants mentioned a need for greater accessibility, the well-educated, tech-savvy nature of our participant sample suggests that the benefits of this technology may not currently be connecting with the full population who need mental health support. One approach to address this could be to create solutions targeted at specific populations or conditions; another could be to find better ways to introduce users to the technology, for example, through the “digital navigator” roles proposed to connect users to DMHIs56,57. In any case, for these tools to remain available, there appears to be a need to develop sustainable business models. While some participants suggested they would be willing to pay for access to generative AI chatbots, research suggests most users would not58, and the path to health insurance funding is not easy59. To illustrate the challenge, Inflection, the company behind the Pi chatbot used by most of the participants in our study, pivoted in March 2024 from providing consumer emotional support services towards enterprise AI services, due to a lack of a business model, and despite USD 1. 5 billion of investment60. Lessons learned from attempts to scale up DMHIs may offer insights here61,62,63,64,65. [SEP] Justify:

Output: The text corroborates the claim by discussing the potential of generative AI chatbots to provide valuable insights into therapy and enhance mental health support. It underscores the ways in which these tools could be made more effective, such as better listening, improved user engagement, and the integration of multimedia interfaces. Additionally, it highlights how understanding the technical realities of AI could increase access to mental health benefits. Overall, the text supports the notion that AI-powered chatbots can play a significant role in mental health contexts.