Input: Task: corroborate the claim using the given text. Claim: Protected areas like national parks help preserve habitats and biodiversity. [SEP] Text: We conducted this analysis both at the entire range and single-country scales. We excluded from this analysis those countries with n records < 5, those not within the EU area, and those which did not feature suitable habitat as predicted by our SDM. We thus calculated the percent of records and of suitable area to S. pedo overlapping with either Natura2000, Nationally protected areas, and their combination. To assess whether the two types of protected areas differed in their efficacy in preserving the species, we ran a repeated measure two-way ANOVA test upon the percent coverage values of each class of protected areas and for type of data (records vs suitable range), using Tukey’s post hoc tests for assessing significance in coverage between each compared pair of values. Association to habitatsWe assessed whether protected habitats as listed in of the provide an effective surrogate of S. pedo’s ecological needs for conservation, by focusing on a local (regional) scale. The grassland layer used for the models, and the habitat suitability raster were first clipped to the regional boundaries’ geographical extent. We then selected three grassland habitats listed in of the and occurring widely across the region and namely (i) semi-natural dry grasslands on calcareous substrates (Directive code: 6210), (ii) pseudo-steppe with grasses and annuals of the Thero-Brachypodietea (6220), and (iii) Eastern sub-Mediterranean dry grasslands (62A0). These grassland types encompass most of the protected dry grassland habitats occurring in low- to mid-altitudes across Southern Europe, and are considered as priority habitats to conservation due to the high-diversity of both plants and invertebrates they host (Valkó et al. 2016). Habitat layers, that have been mapped within the entire regional territory by 2018, were provided by the regional authority as vector polygons ( In order to separately consider grassland surfaces as listed and non-listed habitats, we first excluded all portions of the grassland layer overlapping with any habitat polygons, i. e. leaving only grassland areas listed as “Non-habitat grassland”. To assess the importance of different kinds of grasslands in fostering the occurrence of S. [SEP] Justify:

Output: The text corroborates the claim by discussing the assessment of protected areas and their role in preserving species and habitats. It mentions the 'efficacy in preserving the species' and highlights specific grassland habitats that host high biodiversity of both plants and invertebrates, which supports the notion that protected areas contribute to habitat and biodiversity conservation.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: baumannii is a recognized pathogen of nosocomial transmission and infection (Hamidian & Nigro, 2019). The whole process of sampling in our study was conducted in the hospital. Although the operation was carried out to avoid infection as much as possible, the abundance of A. baumannii in our samples was too low, and there was also the possibility of nosocomial infection. Similarly, previous studies have found the presence of Acinetobacter in the stool of babies delivered vaginally (1 or 2 days), which was ultimately interpreted as a nosocomial infection (Pandey et al. , 2012). We studied the characteristics of the vaginal microbiome during periods A, B, and C in samples from the same sampling site. Our results showed that Lactobacillus had the highest relative abundance in the mid-vagina during periods A, B and C. In contrast, there were significant differences in the microflora of the cervical orifice during periods A and C. These differences were reflected in the enrichment of Pseudomonas and Acinetobacter during period A, while the enrichment of Lactobacillus during period C. The results of functional annotation revealed that the cervical orifice and the mid-vagina microbiome of period A were rich in the metabolic pathway 6-hydroxymethyl-dihydropterin diphosphate biosynthesis III, which was mainly contributed by Chlamydia spp. (Adams et al. , 2014). Based on our results, we considered that there were individuals in the study who were infected with Chlamydia. In addition, tetM gene of tetracycline resistance was enriched in period A, which may indicate that the existence of tetracycline antibiotic resistance gene provides convenient conditions for Chlamydia spp. infection. After further study, we also found that the relative abundance of Proteobacteria gradually decreased, while the relative abundance of Firmicutes gradually increased from period A to period C in the cervical orifice. This trend also existed at the genus level, where the relative abundance of Acinetobacter gradually decreased, while the relative abundance of Lactobacillus gradually increased. The findings revealed once again that probiotics were antagonistic to pathogenic bacteria. In addition, we found that the CSTs of samples No. 10 and No. 11 changed with the menstrual cycle, and the trend of change was from to CST I. [SEP] Justify:

Output: The text contrasts the claim by presenting evidence that microbiome compositions vary significantly among individuals. It notes differences in microbial abundance and composition, such as variations in Lactobacillus and Acinetobacter levels in different periods, indicating that microbiome variability exists across individuals and conditions.


Input: Task: pass the claim using the given text. Claim: Astrobiological data from telescopes is analyzed using advanced statistical methods. [SEP] Text: Δ(cro-ea59):tetR-bla], ΔprfA, ΔtolC0∆RF1n/aAAFitness, conjugation, and viral infection (Figures 3 and 4)GRO. AA. ∆ssrAGRO. AA (this study)ΔmutS:zeo. Δ(ybhB-bioAB):[λcI857. Δ(cro-ea59):tetR-bla], ΔprfA, ΔtolC0∆RF1∆ssrAAAFitness, conjugation, and viral infection (Figures 3 and 4)GRO. AA. ∆arfAGRO. AA (this study)ΔmutS:zeo. Δ(ybhB-bioAB):[λcI857. Δ(cro-ea59):tetR-bla], ΔprfA, ΔtolC0∆RF1∆arfAAAFitness, conjugation, and viral infection (Figures 3 and 4)GRO. AA. ∆arfBGRO. AA (this study)ΔmutS:zeo. Δ(ybhB-bioAB):[λcI857. Δ(cro-ea59):tetR-bla], ΔprfA, ΔtolC0∆RF1∆arfBAAFitness, conjugation, and viral infection (Figures 3 and 4)GRO. AA. ∆ssrA. ∆arfBGRO. AA (this study)ΔmutS:zeo. Δ(ybhB-bioAB):[λcI857. Δ(cro-ea59):tetR-bla], ΔprfA, ΔtolC0∆RF1∆ssrA, ∆arfBAAFitness, conjugation, and viral infection (Figures 3 and 4)GRO. AA. ∆arfA. ∆arfBGRO. AA (this study)ΔmutS:zeo. Δ(ybhB-bioAB):[λcI857. [SEP] Justify:

Output: The text is irrelevant to the claim, as it discusses specific genetic mutants and their fitness in terms of conjugation and viral infection without any mention of astrobiology, telescopes, or statistical methods.


Input: Task: corroborate the claim using the given text. Claim: Protected areas like national parks help preserve habitats and biodiversity. [SEP] Text: pedo by boosting habitat suitability, we calculated the percent amounts of all habitat and non-habitat extents within each suitable grid cell across the region. Subsequently, we quantified the relationship between suitability values and grassland composition by running a Model (GLM), using suitability values as response variable, and the percent amounts of all habitats and non-habitat grasslands as predictors, considering significant those effects with p < 0. 05 and whose confidence intervals did not encompass 0. All analyses were run with R 4. 2. 1 (R 2022). ResultsSpecies distribution modelsOur model reached a satisfactory level of predictive performance at both present and future time, as evaluated by AUC and TSS values (> 0. 90 and > 0. 85, respectively). Saga pedo potential distribution is apparently spread across the known range of the species, with large portions of Mediterranean and continental Europe being classified as potentially suitable, particularly in the eastern Iberian Peninsula, southern France, and peninsular Italy, as well as the Balkan and Carpathian regions (Fig. 2). The main drivers of S. pedo suitability in our models were the mean temperature of the coldest month (bio6; 18. 5% of explained variance), and precipitation seasonality (bio15; 14. 5%), followed by the presence of grasslands (11. 9%), and precipitation of the warmest quarter (bio18; 10. 4%). Overall, the species seems associated with grassland areas characterized by mild winter and dry summer, with predictable rainfall patterns throughout the year (i. e. , low precipitation seasonality). Minor but significant role in influencing the probability of occurrence of the species in our models was also covered by precipitation of coldest quarter (bio19; 6. 0%), suggesting that S. pedo also favors relatively wet winter months. Fig. 2Map of potential suitable range to Saga pedo in Europe, according to ensemble ModelsFull size imageConservation gap analysisThe overall network of protected areas provides protection to ca. 1/3 (31%) of the potential distribution of S. pedo across the EU in which the species occurs. The Natura2000 network currently protects 18% of the species’ potential range, a value comparable to that offered by Nationally protected areas (25%; p > 0. 05). When considering countries separately (Fig. [SEP] Justify:

Output: The text corroborates the claim by referencing that 'the overall network of protected areas provides protection to ca. 1/3 (31%) of the potential distribution of S. pedo across the EU in which the species occurs.' This indicates that protected areas, which include national parks, play a significant role in preserving specific species and thus contribute to habitat and biodiversity conservation.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Throughout the menstrual cycle vaginal CST change are not uncommon. Previous studies have also pointed out that CST changes over time. For example, Gajer et al. ’s study on the vaginal community of healthy women based on 16S sequencing found that during the menstrual cycle, vaginal CST in healthy women changed from type CST I to type and then changed back to type CST I at the end of menstruation, opposite the direction of CST shift in our results (Gajer et al. , 2012). In their study this shift was an individual phenomenon, so we attributed this opposite shift phenomenon to individual differences. Studies have revealed significant differences in microbiomes between the secretory and proliferative phases (luteal and follicular phases) of the menstrual cycle regardless of disease condition or body part (Pelzer et al. , 2018; Sola-Leyva et al. , 2021). Some studies have pointed out that there is a significant pH gradient in the reproductive tract, and the change of vaginal CST during the menstrual cycle may be closely related to vaginal PH (Lykke et al. , 2021). In addition, we thought this shift seems to be related to the spread of microorganisms in the mid-vagina as the cervical orifice opens during periovulatory. The results of our exploration of the vaginal microbiome in Chengdu, Hangzhou, and Shenzhen showed that there were significant differences in the composition of the cervical orifice microbiome in different regions of China. The relative abundance of Lactobacillus in cervical orifice microbiome of women in Chengdu was significantly higher. Although pathogenic bacteria were present in the samples from Hangzhou and Shenzhen, Lactobacillus was the dominant bacterium. At the species level, the samples from Chengdu were enriched with L. crispatus, while the samples from Shenzhen were enriched with G. vaginalis, A. johnsonii, C. trachomatis and M. genitalium. The samples from Hangzhou were enriched with pathogenic microorganisms S. _epidermidis. C. trachomatis is mainly pathogenic to the urogenital tract (Cevenini, Donati & Sambri, 2002). M. genitalium is associated with sexually transmitted diseases (Yueyue et al. , 2022). [SEP] Justify:

Output: The text contrasts the claim by providing evidence that individual differences significantly influence microbiome composition. It cites studies indicating that vaginal CST changes are specific to individuals and vary throughout the menstrual cycle, reflecting diversity in microbiomes. Furthermore, the findings from different regions in China show distinct differences in the cervical orifice microbiome composition, highlighting that not everyone's microbiome is identical.


Input: Task: pass the claim using the given text. Claim: Astrobiological data from telescopes is analyzed using advanced statistical methods. [SEP] Text: Δ(cro-ea59):tetR-bla], ΔprfA, ΔtolC0∆RF1∆arfA, ∆arfBAAFitness, conjugation, and viral infection (Figures 3 and 4) *All strains derived from ECNR2, as described in Wang et al. (2009). †for additional information on strains and sources. The GenBank accession number for E. coli MG1655 is U00096, and the GenBank accession number for GRO. AA is CP006698. ‡ Out of a total of 321 in the original ECNR2 strain. §RF1 terminates translation at UAG and UAA. Deletion of RF1 eliminates recognition of UAG during translation; translational termination continues through RF2, which recognizes UAA and UGA. #The ssrA gene encodes the tmRNA, which appends the ssrA degradation tag to stalled ribosomes. The wild-type sequence is mutation of the C-terminus to slows degradation of peptides to enable detection by mass spectrometry. Table 2 Components of peptide library constructed to search and analyze tandem mass spectrometry data. library was separate from the library that contained the entries of the first three rows of the table (see Supplementary file 3 and 4). Library componentExample peptides (from Figure 2A)Enables detection of…Complete peptide listAny one of 20 canonical amino acids inserted at the UAG codon-cognate suppressionSupplementary file 3Any length of C-tail following UAG codon to the next non-UAG stop codon or to 38 amino acids downstream of the UAG codon, whichever came firstframeshifting, and rescue by ArfA or ArfBSupplementary file 3degradation tagby tmRNA-SmpBSupplementary file 3All peptides of form where X is any amino acidof translational fidelitySupplementary file 4 Key resources table Genetic reagents, bacterial strains, antibodies, and software used in this study. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses genetic strains, methods of peptide analysis, and mass spectrometry, but does not mention astrobiological data or telescopes. Therefore, it does not corroborate or contrast the claim.


Input: Task: corroborate the claim using the given text. Claim: Protected areas like national parks help preserve habitats and biodiversity. [SEP] Text: To assess whether the two types of protected areas differed in their efficacy in preserving the species, we ran a repeated measure two-way ANOVA test upon the percent coverage values of each class of protected areas and for type of data (records vs suitable range), using Tukey’s post hoc tests for assessing significance in coverage between each compared pair of values. Association to habitats We assessed whether protected habitats as listed in of the provide an effective surrogate of S. pedo’s ecological needs for conservation, by focusing on a more local (regional) scale. The grassland layer used for the models, and the habitat suitability raster were first clipped to the regional boundaries’ geographical extent. We then selected three grassland habitats listed in of the HD and occurring widely across the region and namely i) semi-natural dry grasslands on calcareous substrates (HD code: 6210), ii) pseudo-steppe with grasses and annuals of the Thero-Brachypodietea (6220), and iii) Eastern sub-Mediterranean dry grasslands (62A0). These grassland types encompass most of the protected dry grassland habitats occurring in low- to mid-altitudes across Southern Europe, and are considered as priority habitats to conservation due to the high-diversity of both plants and invertebrates they host (Valkó et al. 2016). Habitat layers, that have been mapped within the entire regional territory by 2018, were provided by the regional authority as vector polygons ( In order to separately consider grassland surfaces as listed and non-listed habitats, we first excluded all portions of the grassland layer overlapping with any habitat polygons, i. e. leaving only grassland areas listed as “Non-habitat grassland”. To assess the importance of different kinds of grasslands in fostering the occurrence of S. pedo by boosting habitat suitability, we calculated the percent amounts of all habitat and on-habitat extents within each suitable grid cell across the region. Subsequently, we quantified the relationship between suitability values and grassland composition by running a Model (GLM), using suitability values as response variable, and the percent amounts of all habitats and non-habitat grasslands as predictors, considering significant those effects with p<0. 05 and whose confidence intervals did not encompass 0. [SEP] Justify:

Output: The text corroborates the claim by discussing the effectiveness of protected areas in preserving diverse grassland habitats and their ecological importance. It highlights specific grassland types recognized as priority habitats due to their high diversity of plants and invertebrates, thereby supporting the notion that protected areas contribute to habitat preservation and biodiversity conservation.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: In addition, members of the genus Gardnerella are usually one of the most abundant bacteria in BV (Agarwal & Lewis, 2021). S. _epidermidis is now considered an important opportunistic pathogen, the most common source of infection on indwelling medical devices, and it is now the most common cause of hospital-acquired infections (Otto, 2009). The differences in the female microbiome between different regions were mainly due to the species and abundance of pathogenic microorganisms, but the abundance of these pathogenic or opportunistic pathogens was much lower than the abundance of Lactobacillus and only a few individuals had a high abundance of pathogenic microorganisms. Further analysis results at the functional level showed that the vaginal microbiome of women in Hangzhou was enriched with multiple ARGs, indicating that women in Hangzhou had resistance to vaginal antibiotics. In contrast, women in Chengdu had a healthier vaginal microbiome. Previous studies of virome analysis of vaginal samples from healthy women have shown that most of the identified viral sequences were from double-stranded DNA phages, with eukaryotic viruses accounting for only 4% of total readings (Jakobsen et al. , 2020). In addition to anatomical location, there are many factors related to the structure of vaginal virome, such as diet, age, and geographical location of the sampled individual (Liang & Bushman, 2021). The dominant virus in our results was bacteriophage. Uroviricota was the most abundant virus during periods A, B and C. The phylum Uroviricota includes crAss-like phages, which are common in human enteroviruses. In addition, we found significant differences in the vaginal virome of periods A, B, and C. In period B, multiple viruses were enriched, and two viruses, uncultured crAssphage, and HSV-1, were detected. Studies have shown that crAssphage is the most abundant virus known to exist in humans and one of the most common phages in publicly available metagenomes (Dutilh et al. , 2014). HSV-1 virion is the herpes virus, which can establish a latent infection but when reactivated can cause herpes of the skin or genitalia, conjunctivitis, keratitis, encephalitis or herpes eczema (Kukhanova, Korovina & Kochetkov, 2014). [SEP] Justify:

Output: The text contrasts the claim by highlighting significant differences in microbiome composition for different individuals based on factors such as geographical location, diet, and age. It specifically notes that there are variations in vaginal microbiomes between women in Hangzhou and Chengdu, which indicates that microbiome compositions are not identical among individuals.


Input: Task: pass the claim using the given text. Claim: Astrobiological data from telescopes is analyzed using advanced statistical methods. [SEP] Text: Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional informationReference #Full genotype of strains# 1 statusRibosomal rescue gene knockoutssrA tag statusGene (Escherichia coli)pUAG-GFPthis papereGFP-6xHis -UAG; Plasmid NJM88; Strain NJM1242eGFP protein with a C-terminal 6-His tag for protein purification, terminating translation in a UAG codon. Plasmid NJM88; Strain NJM1242N/AN/AN/AN/AN/AGene (E. coli)pUAA-GFPthis papereGFP-6xHis -UAA; Plasmid NJM89; Strain NJM1249eGFP protein with a C-terminal 6-His tag for protein purification, terminating translation in a UAA codon. Plasmid NJM89; Strain NJM1249N/AN/AN/AN/AN/AGenetic reagent (E. coli)RK2410. 1126/science. 1205822; 10. 1016/j. cels. 2016. 06. 009pRK24; Strain NJM699Conjugative RK2 plasmid (10. 1006/ jmbi. 1994. 1404), but lacks functional AmpR gene. Strain NJM699N/AN/AN/AN/AN/AGenetic reagent (E. coli)(CGSC), Strain #4401pF; Strain EMG2; Strain CGSC#4401; Strain NJM426; Strain NJM473plasmid, as described by PMID: 4568763. Obtained from the Yale CGSC. Strain NJM426; Strain NJM473N/AN/AN/AN/AN/AGenetic reagent (E. coli)pZE21_ UAG-GFPthis paperpZEtR-eGFP -cHis-TAG- v02; Plasmid NJM88; Strain NJM1242pZE21 plasmid with pLtetO promoter driving inducible expression of eGFP with a C-terminal 6-His tag and terminating in UAG codon. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on specific genetic materials, plasmids, and strains associated with Escherichia coli rather than discussing astrobiological data or statistical methods used in analyzing such data from telescopes.


Input: Task: corroborate the claim using the given text. Claim: Protected areas like national parks help preserve habitats and biodiversity. [SEP] Text: ResultsSpecies distribution models Our model reached a satisfactory level of predictive performance at both present and future time, as evaluated by AUC and TSS values (>0. 90 and >0. 85, respectively). Saga pedo potential distribution is apparently spread across the known range of the species, with large portions of Mediterranean and continental Europe being classified as potentially suitable, particularly in the eastern Iberian Peninsula, southern France, and peninsular Italy, as well as the Balkan and Carpathian regions (Figure 2). The main drivers of S. pedo suitability in our models were the mean temperature of the coldest month (bio6, 18. 5% of explained variance), and precipitation seasonality (bio15, 14. 5%), followed by the presence of grasslands (11. 9%), and precipitation of the warmest quarter (bio18, 10. 4%). Overall, the species seems associated with grassland areas characterized by mild winter and dry summer, with predictable rainfall patterns throughout the year (i. e. , low precipitation seasonality). Minor but significant role in influencing the probability of occurrence of the species in our models was also covered by precipitation of coldest quarter (bio19, 6. 0%), suggesting that S. pedo also favors relatively wet winter months. Conservation gap analysis The overall network of protected areas, comprising both NPA and N2K, shows relatively low degree of overlap (31%) with the potential distribution of S. pedo across the EU in which the species occurs. The N2K network currently protects 18% of the species’ potential range, a value comparable to that offered by NPA (25%, p>0. 05). When considering countries separately (Table 1), the highest percent of overall protected range is located in Hungary (38%) and Croatia (25%), while all other countries ranged between 13-22%. When running the same analysis on exact occurrence records, values were, on average, significantly higher (1,23=109. 9, p<0. 001), ranging from 24 to 99%, with 79% of overall observations falling within the N2K/NPA networks across EU. There was no significant difference between the coverage provided by N2K and NPAs, nor any interaction between the type of network and the type or data used (all p>0. 05). Table 1. [SEP] Justify:

Output: The text corroborates the claim by discussing the conservation gap analysis of protected areas, indicating that networks such as NPA and N2K play a significant role in protecting the potential distribution of certain species, such as S. pedo. It highlights that 18% of the species' potential range is protected by N2K and 25% by NPA, thus supporting the idea that protected areas contribute to habitat preservation and biodiversity.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: HSV-1 may also be involved in the pathogenesis of multiple sclerosis (Bello-Morales et al. , 2012). According to our results, it appeared that healthy women during period B were more susceptible to virus invasion and infection of the cervical orifice. conclusion, we conducted metagenomic and virome sequencing of the vaginal microbiome in healthy women. Our results showed that the dominant microflora in the vagina of healthy women was Lactobacillus, and there was no significant difference in the microbiome composition between the cervical orifice and the mid-vagina during the corresponding cycle. We also found that healthy women living in different regions had significantly different vaginal microbiomes, the reasons and mechanisms leading to this difference still need more and more in-depth research to clarify. Our results also showed the relative abundance of Acinetobacter gradually decreased, while the relative abundance of Lactobacillus gradually increased throughout the menstrual cycle. The interaction between probiotics and pathogenic bacteria provides a reference for the preparation of probiotic preparations, and also valuable for the use of clinical antibiotics. Overall, there are also many limitations to this study. Changes in CSTs occur in a small number of individuals, and small sample sizes make it difficult to ensure that the results obtained are universal. The age range of volunteers in this study was 20–30 years old, and it was not possible to determine the vaginal microbiome of healthy women in other age ranges. Future metagenomic studies with larger sample sizes are needed to reveal more information. Results and discussion of separate studies on samples NO. 9 and NO. 14 DOI: 10. 7717/peerj. 16438/supp-1 microbiome composition of the cervical orifice and the mid-vagina (A) Top 10 most abundant bacterial phyla in two different sites of vagina. (B) The comparison of Firmicutes and Proteobacteria in two different sites of vagina (p < 0. 05). (C) Top 10 most abundant bacterial genera in two different sites of vagina. (D) The comparison of Acinetobacter baumannii in two different sites of vagina (p < 0. 05). (E) The difference of genus-level relative abundance between the cervical orifice and the mid-vagina during period A by LEfSe (p < 0. 05 and LDA > 3). DOI: 10. 7717/peerj. [SEP] Justify:

Output: The text contrasts the claim by indicating significant differences in microbiome composition among healthy women living in different regions. Specifically, it notes that 'healthy women living in different regions had significantly different vaginal microbiomes,' which directly refutes the notion that microbiome compositions are identical across individuals. Additionally, the text alludes to the variability in microbiome due to age and sample size limitations, further suggesting that microbiome diversity exists.


Input: Task: pass the claim using the given text. Claim: Astrobiological data from telescopes is analyzed using advanced statistical methods. [SEP] Text: Inducible with anhydro-tetracycline. Plasmid NJM88; Strain NJM1242N/AN/AN/AN/AN/AGenetic reagent (E. coli)pZE21_ UAA-GFPthis paperpZEtR-eGFP -cHis-TAA-v02 Plasmid NJM89; Strain NJM1249pZE21 plasmid with pLtetO promoter driving inducible expression of eGFP with a C-terminal 6-His tag and terminating in UAA codon. Inducible with anhydro-tetracy cline. Plasmid NJM89; Strain NJM1249N/AN/AN/AN/AN/AGenetic reagent (Enteroba cteria phage λ)λ. CI857(CGSC), (contact directly)λ. CI857; λ phage; Phage NJM102Phage λ with temperature- sensitive CI repressor gene; when incubated at 37° C, phage becomes obligate lyticPhage NJM102N/AN/AN/AN/AN/ACell line (E. coli)GRO. DDthis paperC31GIB. tmRNA-DD; Strain #987MG1655-derived strain with all 321 UAG codons mutated to UAA, deletion of RF1, and tmRNA tag C-terminal amino acids mutated from AA to DD. Retains lambda red cassette for recombineering. Investigated in Figure 2. Strain #987ΔmutS:zeo. Δ(ybhB- bioAB) :[λcI857. Δ(cro-ea59) :tetR-bla]. ΔprfA. ΔtolC. tmRNADD0+RF1n/aDDCell line (E. coli)GRO. DD. prfA+this paperC31GIB. prfA+. tmRNA -DD; Strain #996MG1655-derived strain with all 321 UAG codons mutated to UAA, retains RF1, and tmRNA tag C-terminal amino acids mutated from AA to DD. Retains lambda red cassette for recombineering. Investigated in Figure 2. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses various plasmids, strains, and genetic reagents without mentioning telescopes or advanced statistical methods used in analyzing astrobiological data.


Input: Task: corroborate the claim using the given text. Claim: Protected areas like national parks help preserve habitats and biodiversity. [SEP] Text: Conservation coverage for Saga pedo by the networks of protected areas in Europe, expressed as percent per country/area, based on predicted suitable range (Range) and exact records (Occurrences). N2K=Natura2000 network, NPA=Nationally protected areas. Suitable habitat was mapped through species distribution models (SDMs) binarized maps. N2K NPA N2K+NPA CountryArea (n records) (91) 12% 93% 11% 38% 14% 95% France (2,400) 16% 22% 18% 25% 22% 24% Italy (84) 11% 88% 11% 58% 13% 90% Croatia (20) 24% 75% 22% 75% 25% 75% Austria (122) 33% 99% 25% 90% 33% 99% Hungary (15) 35% 60% 21% 55% 38% 60% Bulgaria (32) 21% 72% 11% 68% 24% 75% Romania (40) 15% 55% 12% 50% 15% 55% Europe (3,278) 18% 72% 25% 58% 31% 73% Association to habitats A total of 348 grid cells are environmentally suitable to S. pedo in Apulia according to our model, mostly falling within the Murgia plateau and its immediate surroundings, besides some suitable spots located on the grassy slopes of the Gargano massif. Probability of occurrence of S. pedo within cells predicted to be suitable significantly varied according to grassland composition, with a positive effect of the amounts of 62A0 (Eastern sub-Mediterranean dry grasslands) only, and a negative effect of non-habitat grasslands (Table 2, Figure 3). Table 2. Effects of grassland habitats on the environmental suitability to Saga pedo obtained species distribution models, estimated by generalized linear models. Habitat 6210, Habitat 6220 and Habitat 62A0 correspond to protected grasslands listed within the of the EU Habitats Directive). Predictors are quantified as percent amount of habitat within 1x1 km grid cells. Estimates std. Error 95% CI p Non-habitat grassland -0. 004 0. 001 -0. 006 – -0. 002 <0. 001 Habitat 6210 -0. 003 0. 002 -0. 008 – 0. 001 0. [SEP] Justify:

Output: The text corroborates the claim by detailing how protected areas in Europe, such as the Natura 2000 network and nationally protected areas, contribute to the conservation of habitats suitable for species like Saga pedo. It underscores the importance of these protected areas in maintaining suitable habitats, as mentioned by stating that a total of 348 grid cells environmentally suitable to S. pedo were identified, primarily within protected regions. The inclusion of specific habitat types listed in the EU Habitats Directive further supports the notion that protected areas play a vital role in biodiversity conservation.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: 16438/supp-2 microbiome composition during periods A, B and C (A) Top 10 most abundant bacterial phyla of vagina in three different periods. (B) Top 10 most abundant bacterial genera of vagina in three different periods. (C) The difference of phylum-level relative abundance between periods A, B and C in the cervical orifice by LEfSe (p < 0. 05 and LDA > 3). (D) Dynamics of the relative abundance of Proteobacteria in the cervical orifice throughout the menstrual cycle. (E) Dynamics of the relative abundance of Lactobacillus in the cervical orifice throughout the menstrual cycle. (F) Dynamics of the relative abundance of Acinetobacter in the cervical orifice throughout the menstrual cycle. (G) Dynamics of the relative abundance of Lactobacillus in the mid-vagina throughout the menstrual cycle. (H) Dynamics of the relative abundance of L. iners in the cervical orifice throughout the menstrual cycle. (I) Dynamics of the relative abundance of L. iners in the mid-vagina throughout the menstrual cycle. DOI: 10. 7717/peerj. 16438/supp-3 statistics DOI: 10. 7717/peerj. 16438/supp-4 All sample collection and vaginal microecological tests were carried out at Hospital. We would like to thank the technical support provided by Hospital and every volunteer who participated in the study. authors declare there are no competing interests. performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the 4917. research was supported by the (2023NSFSC1935). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 2014. Promiscuous and adaptable enzymes fill holes in the tetrahydrofolate pathway in Chlamydia species. MBio 5:e01378–01314 Agarwal K, Lewis AL. 2021. Vaginal sialoglycan foraging by Gardnerella vaginalis: mucus barriers as a meal for unwelcome guests? Glycobiology 31:667-680 Alonzo Martínez MC, Cazorla E, Cánovas E, Martínez--López V. 2021. Study of the vaginal microbiota in healthy women of reproductive age. [SEP] Justify:

Output: The text presents data on the microbiome composition, highlighting variations in relative abundance among different bacterial phyla and genera during specific periods of the menstrual cycle. It suggests that microbiome composition is dynamic and varies based on specific conditions rather than being identical across individuals.


Input: Task: pass the claim using the given text. Claim: Astrobiological data from telescopes is analyzed using advanced statistical methods. [SEP] Text: Strain #996ΔmutS:zeo. Δ(ybhB- bioAB) :[λcI857. Δ(cro-ea59): tetR-bla]. ΔtolC. tm RNADD0∆RF1n/aDDCell line (E. coli)ECNR210. 1016/j. cels. 2016. 06. 009ECNR2. Δmut S:zeocin. Δ λRed; Strain #795MG1655-derived strain that contains 321 UAG codons and retains RF1. Investigated in Figures 3 and 4. Strain #795ΔmutS:zeo321+RF1n/aAACell line (E. coli)GRO. AA10. 1016/j. cels. 2016. 06. 009C31. final. ΔmutS: zeocin. ΔprfA. ΔλRed; Strain #796MG1655-derived strain with all 321 UAG codons mutated to UAA, deletion of RF1. Investigated in Figures 3 and 4. Strain #796ΔmutS: zeo. ΔprfA (GenBank ID: CP006698)0∆RF1n/aAACell line (E. coli)GRO. AA. ∆arfBthis paperC31GIB. arfB: tolCorf. ΔλRed; Strain #1230MG1655-derived strain with all 321 UAG codons mutated to UAA, deletion of RF1, and deletion of arfB. Investigated in Figures 3 and 4. Strain #1230ΔmutS: zeo. ΔprfA. arfB:tolC0∆RF1∆ssrAAACell line (E. coli)GRO. AA. ∆ssrAthis paperC31GIB. ssrA :tolC. ΔλRed; Strain #1231MG1655-derived strain with all 321 UAG codons mutated to UAA, deletion of RF1, and deletion of ssrA. Investigated in Figures 3 and 4. Strain #1231ΔmutS: zeo. ΔprfA. [SEP] Justify:

Output: The text is irrelevant to the claim regarding astrobiological data and telescopes. It discusses various strains of E. coli and their genetic variations, lacking any mention of astrobiology or statistical methods related to telescope data analysis.


Input: Task: corroborate the claim using the given text. Claim: Protected areas like national parks help preserve habitats and biodiversity. [SEP] Text: Protected areasProtected area refers to as geographical space, recognized, dedicated, and managed through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values [1]. The definition of protected area is expanded into six categories. The first category is further divided into two subcategories, i. e. , strict nature reserve and wilderness area. Strict nature reserve is designated for the conservation of biodiversity and geological and geomorphological features. In this reserve, visitation, use, and impacts of humans are controlled and limited to ensure that the conservation values are protected. Meanwhile, the wilderness area normally deals with large unmodified or slightly modified areas that retain their natural character and influence. To ensure that natural conditions are protected and preserved, the areas are without permanent or significant human habitation. The second category refers to national park which consists of large and natural areas that protect large-scale ecological processes with characteristic species and ecosystem. National park also provides scientific, spiritual, educational, recreational, and visitor opportunities. The third category is identified as natural monument or feature. These areas are set aside for the protection of specific natural monuments. These include landform, sea mount, marine cavern, and ancient grove: some geological feature such as cave and living feature such as an ancient grove. The fourth category is habitat or species management areas in which the management is prioritized to protect particular species or habitat. Active and regular interventions are required to meet the needs of particular species and habitats. In the fifth category, i. e. , protected landscape and seascape, interaction of people and nature over time may produce distinct character of protected area which includes significant ecological, biological, cultural, and scenic value. Safeguarding integrity of this interaction is crucial to ensure the protection and sustainability of the area. The final category refers to as protected areas with sustainable use of natural resources. This involves an integration of ecosystem conservation, cultural values, and natural resource management which involve large and natural area condition. One of the aims for this category is for the use of natural resources that is compatible with nature conservation. Advertisement 3. Biodiversity conservationBiodiversity can be defined as the variety of plants, animals, and microorganisms that exist, the genes they contain, and the ecosystems they live in. It provides a variety of goods and services and supports our economy and lifestyles. [SEP] Justify:

Output: The text corroborates the claim by detailing the definition and categories of protected areas, including national parks, which are explicitly mentioned as places that protect large-scale ecological processes and characteristic species. It emphasizes that strict nature reserves and other categories prioritize biodiversity conservation, thus supporting the assertion that such protected areas are vital for preserving habitats and biodiversity.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Microorganisms 9:1069. 2020. Hormonal contraception alters vaginal microbiota and cytokines in adolescents in a randomized trial. 11:5578 Bello--Ramos A, Tabarés E, Alcina A, López-Guerrero JA. 2012. Role of the small GTPase Rab27a during herpes simplex virus infection of oligodendrocytic cells. BMC Microbiology 12:265. 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114-2120. 2020. The vaginal metabolome and microbiota of cervical HPV-positive and HPV-negative women: a cross-sectional analysis. Bjog 127:182-192. 2015. Fast and sensitive protein alignment using. 12:59-60. 2022. Ovarian microbiota, ovarian cancer and the underestimated role of HPV. of 23:16019. 2002. Chlamydia trachomatis—the agent. & Obstetrics & Gynaecology 16:761-773. 2017. The microbiota continuum along the female reproductive tract and its relation to uterine-related diseases. 8:875. 2014. A highly abundant bacteriophage discovered in the unknown sequences of human faecal metagenomes. 5:4498. 2018. Species-level functional profiling of metagenomes and metatranscriptomes. 15:962-968. 2005. Molecular identification of bacteria associated with bacterial vaginosis. Journal of Medicine 353:1899-1911. 2012. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150-3152 ütte. 2012. Temporal dynamics of the human vaginal microbiota. Medicine 4:132ra152. 2020. Diversity of vaginal microbiome in pregnancy: deciphering the obscurity. Frontiers in 8:326 Hamidian M, Nigro SJ. 2019. Emergence, molecular mechanisms and global spread of carbapenem-resistant Acinetobacter baumannii. 5:e000306 3rd. 2013. Effects of tampons and menses on the composition and diversity of vaginal microbial communities over time. Bjog 120:695-704 discussion 704–696 Hillmann B, Al-Ghalith GA, Shields-. 2018. Evaluating the information content of shallow shotgun metagenomics. [SEP] Justify:

Output: The text contrasts the claim by referencing various studies on the diversity of microbial communities, implying significant differences in microbiome compositions among individuals. For example, it mentions 'the microbiota continuum along the female reproductive tract and its relation to uterine-related diseases,' which suggests variability rather than uniformity in microbiome compositions.


Input: Task: pass the claim using the given text. Claim: Astrobiological data from telescopes is analyzed using advanced statistical methods. [SEP] Text: ssrA:tolC0∆RF1∆arfAAACell line (E. coli)GRO. AA. ∆arfAthis paperC31GIB. arfA :tolC. ΔλRed Strain #1232MG1655-derived strain with all 321 UAG codons mutated to UAA, deletion of RF1, and deletion of arfA. Investigated in Figures 3 and 4. Strain #1232ΔmutS: zeo. ΔprfA. arfA:tolC0∆RF1∆arfBAACell line (E. coli)GRO. AA. ∆ssrA. ∆arfBthis paperC31GIB. ΔarfB. ssrA:tolC. Δ λRed; Strain #1233MG1655-derived strain with all 321 UAG codons mutated to UAA, deletion of RF1, and deletion of ssrA and arfB. Investigated in Figures 3 and 4. Strain #1233ΔmutS: zeo. ΔprfA. ΔarfB. ssrA:tolC0∆RF1∆ssrA. ∆arfBAACell line (E. coli)GRO. AA. ∆arfA. ∆arfBthis paperC31GIB. Δarf B. arfA:tolC. ΔλRed; Strain #1234MG1655-derived strain with all 321 UAG codons mutated to UAA, deletion of RF1, and deletion of arfA and arfB. Investigated in Figures 3 and 4. Strain #1234ΔmutS: zeo. ΔprfA. ΔarfB. arfA :tolC0∆RF1∆arfA. ∆arfBAAAntibodymouse anti-GFP antibodyotherInvitrogen (Ref#: 332600, Lot#: 1513862A)Invitrogen (Ref#: 332600, Lot#: 1513862A); (5. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses genetic modifications in E. coli strains and related experimental details, which do not pertain to astrobiological data or statistical methods used in its analysis.


Input: Task: corroborate the claim using the given text. Claim: Protected areas like national parks help preserve habitats and biodiversity. [SEP] Text: Man has a moral duty to conserve it to ensure long-term sustainability for human survival and future generations. Trees are an example of critical component of biodiversity. The diversity of life that a tree can support is incredible. For example, in the tropical rainforests, a single tree can house up to 2000 different species of insects, birds, amphibians, reptiles, mammals, fungi, mosses, and epiphytic plants. Unfortunately, natural habitats everywhere are declining. Therefore, the establishment of protected area networks is essential for biodiversity conservation and thus helps to reduce its loss. As habitats are lost, we are also losing various types of precious flora and fauna. No one would have thought, even a few years ago, that some species from the families of Dipterocarpaceae, Grammitidaceae, and Begoniaceae could be extinct, but now they are. Protected areas can be used as complementary measures to achieve sustainable use of biodiversity and protect many threatened and endemic species from becoming vanished. Advertisement 4. Plant conservation strategy: Malaysia’s perspectiveIn many tropical countries throughout the world, much of the forest has lost as a result from rapid changes in land use and land cover since the last few decades. In Malaysia, for example, the changes in land use cover, particularly to agricultural land, rubber, and oil palm cultivation, are the major contributors of this conversion when the country gained independence. For example, in the 1960s, the 70% of land in was under natural forest cover; however, 10 years later, only 60. 9% of forested area remained due to massive land development schemes during that period [2]. By 1980, the natural forest cover further declined to 49. 4% (Forest Statistics, (1979–1985)) and has now stabilized at 44. 5% since 1997 [3] as most of the land more suitable to agriculture has been cleared, leaving hilly, mountainous, and marginal lands and protected areas for wildlife sanctuaries. Malaysia is well endowed with a great biodiversity with about 12,500–15,000 species of vascular plants [4]. A national strategy for plant conservation has been developed for the country. Part of the strategies includes a publication of of in four volumes since 2005 which described a total of 991 species. Meanwhile, the of Sabah and Sarawak was published in seven volumes since 1990 covering a total of 2055 species [5]. [SEP] Justify:

Output: The text supports the claim by explaining the necessity of protected areas for biodiversity conservation. It emphasizes that 'the establishment of protected area networks is essential for biodiversity conservation and thus helps to reduce its loss.' This indicates that protected areas play a critical role in preserving habitats and various species, aligning closely with the claim made.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Comparative analysis of fecal microflora of healthy full-term Indian infants born with different methods of delivery (vaginal vs cesarean): Acinetobacter sp. prevalence in vaginally born infants. Journal of Biosciences 37:989-998. 2018. A role for the endometrial microbiome in dysfunctional menstrual bleeding. Leeuwenhoek 111:933-943. 2011. Vaginal microbiome of reproductive-age women. Proceedings of the of Sciences of the of America 108:4680-4687. 2011. Metagenomic biomarker discovery and explanation. 12:R60 ánez. 2019. Composite analysis of the virome and bacteriome of HIV/HPV co-infected women reveals proxies for immunodeficiency. Viruses 11:422 Sola-Leyva A, Andrés-León E, Molina NM, Terron-Camero LC, Plaza-Díaz J, Sáez-ánchez R, Ruíz S, Martínez L, Altmäe S. 2021. Mapping the entire functionally active endometrial microbiota. 36:1021-1031 -. 2020. Daily vaginal microbiota fluctuations associated with natural hormonal cycle, contraceptives, diet, and exercise. MSphere 5:e00593-20. 2017. The role of lactic acid production by probiotic Lactobacillus species in vaginal health. Research in Microbiology 168:782-792. 2013. Hormonal contraception is associated with a reduced risk of bacterial vaginosis: a systematic review and meta-analysis. 8:e73055. 2006. Antagonistic activity against pathogenic bacteria by human vaginal lactobacilli. Anaerobe 12:221-226. 2020. The alterations of vaginal microbiome in HPV16 infection as identified by shotgun metagenomic sequencing. Frontiers in Cellular and 10:286. 2019. Benchmarking metagenomics tools for taxonomic classification. Cell 178:779-794. 2022. Pathogenicity and virulence of Mycoplasma genitalium: unraveling Ariadne’s thread. Virulence 13:1161-1183. 2021. Contribution of lactobacillus iners to vaginal health and diseases: a systematic review. [SEP] Justify:

Output: The text contrasts the claim by providing evidence of variability in microbiome composition based on factors such as methods of delivery (vaginal vs cesarean) and other distinct biological factors, indicating that microbiome compositions are not identical among individuals.


Input: Task: pass the claim using the given text. Claim: Astrobiological data from telescopes is analyzed using advanced statistical methods. [SEP] Text: 5 μL antibody in 3 mL Milk + TBST)N/AN/AN/AN/AN/AN/AAntibodygoat anti-mouse antibodyotherAbCam (Ref#: ab7023, Lot#: GR157827-1)AbCam (Ref#: ab7023, Lot#: GR157827-1); (2. 2 μL antibody in 10 mL Milk + TBST)N/AN/AN/AN/AN/AN/ARecombinant DNA reagentssrA:tolCthis paper; for use, see tolC positivenegative selection in 10. 1038/nprot. 2014. 081ds111The E. coli native tolC gene used to delete ssrA gene via recombineering (10. 1038/nprot. 2008. 227). ds111N/AN/AN/AN/AN/ARecombinant DNA reagentarfA:tolCthis paper; for use, see tolC positivenegative selection in 10. 1038/nprot. 2014. 081ds112The E. coli native tolC gene used to delete arfA gene via recombineering (10. 1038/nprot. 2008. 227). ds112N/AN/AN/AN/AN/ARecombinant DNA reagentarfB:tolCthis paper; for use, see tolC positivenegative selection in 10. 1038/nprot. 2014. 081ds113The E. coli native tolC gene used to delete arfB gene via recombineering (10. 1038/nprot. 2008. 227). ds113N/AN/AN/AN/AN/ASoftware, algorithmDoubling time algorithm10. 1126/ science. 1241459Growth_ Analyze_ GK. mDoubling time used in 10. 1126science. 1241459, written by in the lab of Dr. George Church. N/AN/AN/AN/AN/AN/ASoftware, algorithmMaxQuant v1. 5. 1. 2otherN/ACommercial software for mass spectrometry analysis. [SEP] Justify:

Output: The text does not address the claim about astrobiological data or its analysis using advanced statistical methods. Instead, it discusses various antibodies and software related to recombinant DNA and mass spectrometry analysis. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Protected areas like national parks help preserve habitats and biodiversity. [SEP] Text: This was done by overlying the spatial location of endangered species onto the image of habitat areas. The purpose is not only to visualize the present data but also to analyze how the trees are spatially distributed in the landscape. This will allow the researchers to address a critical issue in monitoring the endangered species and in identifying priorities for protected area management with regard to boundaries of reserves. The spatial distribution of five endangered species is shown in Figure 1. From the map, the distribution of four species, i. e. , D. semivestitus, S. macrantha, H. apiculata, and V. flavida, in the landscape is appeared to be spatially dispersed. However, the spatial distribution of S. hemsleyana appears to be more localized compared to other species. It is important to note that many of the species appear to be located at the forest edges. Forest edges are prone to disturbance and experience more dramatic environmental changes. For instance, wind and sun dry out the forest edges which results in elimination of water sources for this sensitive habitat of freshwater swamp forest. Forest edges are also more exposed to vegetation clearing and urban development. Therefore, to address the further decline of biodiversity of tree species, it is essential that conservation plan be incorporated in the campus development plan and more efforts be undertaken to conserve the threatened habitat and species. Figure 1. Spatial distribution of five critically endangered species of Dipterocarpaceae at freshwater swamp forest of [6, 7]. From the field survey, for S. hemsleyana the highest number of individuals discovered in the study area is recorded (i. e. , 198), followed by D. semivestitus (35), S. macrantha (24), H. apiculata, (14) and V. flavida (6) (Table 2). For D. semivestitus, the number of individuals found has reduced from what was previously reported. Chua et al. [8] earlier reported that a total of 53 trees were located in this area. The loss of 18 D. semivestitus individuals was due to an illegal logging in the area which took place in 2012 and lightning strike in May 2014. Historical records reported that the species grows on in two places, Reserve (the study location) and Murabahah district, Central Kalimantan, Indonesia [9]. [SEP] Justify:

Output: The text corroborates the claim by discussing the importance of conservation efforts in protected areas, specifically mentioning that species are threatened by disturbances at forest edges and illegal logging. It highlights that urgent conservation plans are needed to protect these species and their habitats, which supports the assertion that protected areas are crucial for preserving biodiversity.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Gut microbiome composition differences among breeds impact feed efficiency in swine access Published: 22 July 2020 Gut microbiome composition differences among breeds impact feed efficiency in swine Matteo Bergamaschi1, : orcid. org/0000-0002-4358-92361, Jeremy Howard3, Huang3, Kent A. Gray3, Constantino Schillebeeckx2, Nathan P. McNulty2 & …Christian Maltecca1 Show authors Microbiome 1. Significant (P < 0. 05) differences were observed. Across time points, ADG for the three breeds rang Fig. 1Average daily gain and feed efficiency (least squares mean ± confidence interval) of Duroc (DR), Landrace (LR), and (LW) at three time points 73 days (T1), 123 days (T2), and 158 days (T3) in the growth trialFull size imageed from 0. 48 to 0. 77 kg/d. At 123 days, ADG was significantly higher in LW (0. 68 ± 0. 058 kg/d) than DR (0. 64 ± 0. 057 kg/d) and LR (0. 65 ± 0. 055 kg/d), while no statistically significant differences were observed at 73 and 158 days. Average daily feed intake ranged from 1503 to 2795 g/d across time points. Average daily feed intake was significantly higher in LW at 123 and 158 days than in DR and LR, while no significant differences were observed at 73 days. The FCR of the three breeds on average ranged from 3. 21 to 3. 80 across time points. Feed conversion ratio was significantly lower in LR at 73 and 123 days compared to DR and LW. With regard to the residual feed intake, RF1 ranged from − 205 to 95, while RF2 ranged from − 226 to 136 across time points. These significant differences between breeds were confirmed by linear discriminant analysis (LDA) (Fig. 2). The first component explained 93. 4% and the second 6. 6% of the total variance. The first component differentiated DR and LR from LW based on FCR (loading 10. 4) and IMF (loading − 0. 18), while the second component discriminated DR and LR based on FCR (loading 8. 4) and backfat (loading − 0. 25). Fig. [SEP] Justify:

Output: The text contrasts the claim by highlighting significant differences in gut microbiome composition among different swine breeds. It mentions that 'Significant (P < 0. 05) differences were observed' and provides details about how these differences impact feed efficiency, indicating that microbiome composition is not identical across individuals, particularly across breeds.


Input: Task: pass the claim using the given text. Claim: Astrobiological data from telescopes is analyzed using advanced statistical methods. [SEP] Text: Skip to main content in a journal Publish with us Track your research 34. 9910 units per month Download 29. 95 Price excludes VAT (USA) Available as PDF Read on any device Instant download Own it forever Buy 59. 99 Price excludes VAT (USA) Available as EPUB and PDF Read on any device Instant download Own it forever Buy e79. 99 Price excludes VAT (USA) Compact, lightweight edition Dispatched in 3 to 5 business days Free shipping worldwide - see info 109. 99 Price excludes VAT (USA) Durable hardcover edition Dispatched in 3 to 5 business days Free shipping worldwide - see info calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Notes1. The DF-GLS unit root test uses generalized least squares (GLS) regression to de-trend the data. 2. According to Hoechle (2007), an AR process can be approximated by a MA process. 3. model is an extension of the Box–Jenkins autoregressive moving average (ARIMA) model with exogenous variables. This particular example does not include the moving average (MA) as an independent variable. The decision to include MA term is determined by observing the autocorrelation function of the dependent variable. Although not shown, including the MA term in this example, does not change the results. For more information on the ARIMA model see Box and Jenkins (1970). 4. For more information on the diffuse option, see the Time-Series Manual, Release 16 and Ansley and Kohn (1985) and Harvey (1989). 5. For more information on inverse roots, see the Time-Series Manual, Release 16 and Hamilton (1994). 6. For more information on unit root tests for panel data, see Data/Release 16. 7. For information on the other tests, please see Herwartz et al. (2018). 8. For more information on the use of these tests, see and Sarafidis (2006). 1316. 297. 5926. pdf Breusch, T. S. (1978). Testing for autocorrelation in dynamic linear models. Papers, 17(31), 334–355. 195. 496. 431. 560. 428. 7237. htm Elliott, G. , Rothenberg, T. J. , & Stock, J. H. (1996). for an Root. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses various purchasing options, publication details, and mentions some statistical methodologies like the DF-GLS unit root test and ARIMA model without making any reference to astrobiology or telescopes. Therefore, it does not provide any corroboration or refutation related to the analysis of astrobiological data.


Input: Task: corroborate the claim using the given text. Claim: Protected areas like national parks help preserve habitats and biodiversity. [SEP] Text: The establishment of protected areas is extremely important in particular for endangered species and sensitive habitats and should be included as one of the key issues of sustainable development elsewhere. However, they need to be better located and actively managed to deal with problems of such as illegal logging, human settlements, unsustainable tourism, encroachment, and challenges in climate change. Unfortunately, in some countries, the protected areas are managed by different networks and governed by different laws with varying degrees of protection status. Therefore, each country should establish uniform national protected areas system operating in the country. Regardless of values and benefits of protected areas, implementation of effective management and conservation strategies aimed at maintaining or restoring these benefits are deemed necessary. 1. Dudley N, editors. Guidelines for Categories. Gland, Switzerland: IUCN; 2008. 86 p2. . Conservation of Dipterocarpaceae in Peninsular Malaysia. Journal of Science. 2000;12(3):593-6153. Anonymous. Survey. United Kingdom: Institute; 19974. Latiff A. The current status of biodiversity conservation in Malaysia. In: Workshop on & Biodiversity, December 13-14, 2010; UKM. 20105. Saw LG. Plant conservation efforts in Peninsular Malaysia. In: on to and Biodiversity. Tokyo, Japan: National Inst. for (NIES) and Hiroshima University; September 25-26, 20126. . Spatial distribution and demographic structure of the critically endangered Dipterocarpaceae in fragmented habitat in Malaysia. In: Poster presented at the of Organizations (IUFRO); October 5-11, 2014;. 20147. . Demographic structure of the critically endangered species of Dipterocarpaceae in freshwater swamp forests in Perak, Malaysia. In: Poster presented at 21st (BSGC); December 15-17, 2016; University of Malaya, Kuala Lumpur. 20168. . Red List: Dipterocarpaceae. No. 129. Malaysia: and Ministry of and Environment; 2010. 30 p9. Ashton PS, editors. Dipterocarpaceae. Series. Vol. 1(9). Kuala Lumpur: (FRIM); 1982. 1. Introduction2. Protected areas3. Biodiversity conservation4. Plant conservation strategy: Malaysia’s perspective5. Endangered species conservation6. Conclusion 1. Choose citation style Select style to clipboard Get citation 2. [SEP] Justify:

Output: The text corroborates the claim by emphasizing the importance of protected areas for endangered species and sensitive habitats, stating that their establishment should be a key issue in sustainable development. It stresses that while protected areas are beneficial, they must be well-located and actively managed to address challenges such as illegal logging and climate change, supporting the notion that protected areas play a crucial role in preserving biodiversity.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: 2Linear discriminant analysis (LDA) of feed efficiency and fatness trait data from three breeds of pig. Scores [a] and loadings [b] are reportedFull size imageGut microbiome composition in the Duroc, Landrace, and breedsFigure 3 illustrates the relative abundance of microbial ASV when aggregated at the family level for the three breeds and the three time points surveyed in this study. Over the three time points, about 80% of ASV were classified into just 7 families: and Lachnospiraceae. Fig. 3Relative abundance of microbiome taxa at family level of Duroc (DR), Landrace (LR), and (LW) in three time points 73 days (T1), 123 days (T2), and 158 days (T3) of the feeding trialFull size imageAlpha diversity of pig gut microbiome was measured using the Shannon, Simpson, and indices (Fig. 4). On average, the alpha diversity across time points ranged from 4. 05 to 4. 43, from 0. 93 to 0. 95, and from 18. 7 to 27. 2 for the Shannon, Simpson, and indices, respectively. Comparing the breeds across time points, Shannon index values were significantly (P < 0. 05) higher in LR than in DR and LW at 123 days. Duroc pigs had lower Shannon index values than LR and LW at 73 days, while no statistically differences were observed at 123 and 158 days. Simpson index values were lower in DR than in LR and LW at 73 and 123 days. Simpson index was significantly lower in DR than in LR and LW at 73 days. Landrace had a higher index than DR and LW at 123 days, while no statistically significant differences were observed at 158 days. Fig. 4Measurements of fecal microbiome alpha diversity at ASV level using the Shannon, Simpson, and indices (least squares means ± confidence interval) in the Duroc (DR), Landrace (LR), and (LW) breeds for three time points 73 days (T1), 123 days (T2), and 158 days (T3) in the growth trial. Full size imageClustering analyses were focused on identifying clusters (enterotypes) among the fecal samples of pigs collected at each time point. Each of these enterotypes may be driven by specific genera that contribute to microbial compositions. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that the gut microbiome composition varies among different breeds of pigs, as highlighted by specific differences in alpha diversity and significant variations in Shannon and Simpson index values across time points. This indicates that microbiome compositions are not identical among individuals.


Input: Task: pass the claim using the given text. Claim: Astrobiological data from telescopes is analyzed using advanced statistical methods. [SEP] Text: Econometrica, 64(4), 813–836. 414. 701. 1301. 150. 196. 312. 6787. html Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. 1117. 2002. 8385. html Download 8. 11 8. 11 8. 2. 1. 1. Fig. 8. 2. 1. 1. Enrollment, Tuition, and Unemployment,// Time (1970 to 2017) twoway (line lnenpub2yr year, lcolor(black) lpattern(solid)) (line lntupub2yr year,// lcolor(black) lpattern(dash)) (line lnunemprate year, lcolor(black) lpattern(dot)),// xlabel(1970 (6) 2017, labsize(small)) ytitle(Logs) title("Trends in Enrollment in// 2 YR, Tuition at 2 YR, and Unemployment Rates" "1970 to 2017", size(medium)) *conduct the DF-GLS tests dfgls lnenpub2yr dfgls lntupub2yr dfgls lnunemprate *create Fig. 8. 2. 1. 2. Enrollment, Tuition, and Unemployment,// First-Differenced (1971 to 2017), take note of options twoway (line D1. lnenpub2yr year, lcolor(black) lpattern(solid)) (line D1. lntupub2yr year,// lcolor(black) lpattern(dash)) (line D1. lnunemprate year, lcolor(black) lpattern(dot)),/// xlabel(1971 (5) 2017, labsize(small)) ytitle(Change in Logs)// title("First-Differenced//Enrollment in 2 YR, Tuition at 2// YR, and Unemployment" "1971 to 2017", size(small)) *regress the first-differenced log of enrollment on the first-differenced log of tuition// and unemployment reg D1. lnenpub2yr D1. lntupub2yr D1. [SEP] Justify:

Output: The text does not address astrobiological data or telescopes, nor does it mention advanced statistical methods. Instead, it discusses econometric tests and trends in enrollment, tuition, and unemployment. As such, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: The illegal wildlife digital market: an analysis of Chinese wildlife marketing and sale on online by Press: 14 July 2020andMackey [Opens in a new window] Show author details Affiliation: Policy Institute, 8950 124, San Diego, CA92037, USA University of California, – Extension, Department of and : Policy Institute, 8950 124, San Diego, CA92037, USA University of California, – Extension, Department of and of and Engineering, University of * Affiliation: Policy Institute, 8950 124, San Diego, CA92037, USA University of California, – Extension, Department of and of Anesthesiology, University of California, School of of Health, University of California, School of Medicine, Department of * Author for correspondence: K Mackey, Email: tmackey@ucsd. edu 0. 38 mb) View PDF [Opens in a new window] Save to to Save to & Permissions [Opens in a new window] an estimated US$19 billion, the illicit wildlife trade is a serious threat to global conservation efforts. This criminal enterprise is now digital, expanding its footprint to consumers internationally by using the Internet and social media platforms. Recent studies have detected illegal wildlife selling posts on the popular social networking site Facebook in several different languages, including Chinese. In order to further explore this challenge to conservation, this study used big data approaches to identify and characterize wildlife trading activity in Chinese language on Facebook using an automated web scraper. We focused on keywords associated with elephants, rhinos and hawksbill turtles. We collected 10 303 unique Facebook posts over a 45-day period and were able to identify 639 posts from 268 unique users, which we suspect of directly marketing the sale of wildlife products. We also identified other species including Tibetan antelope, bears and African spurred tortoises. Facebook community pages appeared to have the highest percentage (48. 2%) of wildlife selling posts. We also identified 14 different countries and regions with suspected wildlife-selling users, most located in Taiwan. Furthermore, we observed that the language used by some sellers changed from descriptive text to emojis and other code words. Collective action is needed from governments, law enforcement, civil society and technology companies leveraging big data approaches to better detect and interdict online Chinese-language wildlife trafficking. Keywords big dataFacebookonline traffickingsocial mediawildlife product saleswildlife trafficking Conservation Issue 3 September 2020 10. [SEP] Justify:

Output: The text corroborates the claim by discussing the significant threat illegal wildlife trafficking poses to global conservation efforts. It emphasizes the need for collective action from various stakeholders, including governments and civil society, to combat wildlife trafficking, thereby implying the involvement of conservation organizations in these efforts.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: The clusters of samples and genera that significantly separate the enterotypes according to breed and time points are shown in Fig. 5 and S1. The optimal number of clusters based on Calinski–Harabasz index maximization was 2, with the exception of 73 and 123 days for DR and LW samples where the optimal number of clusters was more than 2. In order to identify specific bacterial genera that were characteristic to the three breeds within each time point, we performed an LDA analysis coupled with. Figure 5 shows the genera that were differentially represented among the three breeds and time points. Anaerostipes and Turicibacter genera had very high LDA scores across all breeds and enterotypes. At 73 days, the three enterotypes in DR pigs were distinguished by Dorea, Faecalibacterium, and Anaerovibrio, while the enterotypes for LR and LW pigs were distinguished by Anaerostipes and Turicibacter. At 123 days, the four enterotypes in DR pigs were distinguished by a total of 12 genera. Of these, Turicibacter, Faecalibacterium, Anaerostipes, and Dorea were most abundant in enterotypes A, B, C, and D, respectively. At the same time point, the two enterotypes for LR were significantly (P < 0. 05) distinguished for either Turicibacter or Anaerostipes, while the three enterotypes for LW were significantly (P < 0. 05) distinguished by Turicibacter, Anaerostipes, and Clostridium. and B for DR and LR at 158 days were significantly (P < 0. 05) distinguished by Turicibacter and Anaerostipes, while the two enterotypes for LW were distinguished by 11 genera. Of these, Anaerostipes and Sporobacterium were significantly (P < 0. 05) more abundant. This analysis revealed a different grouping of the samples across time points for DR and LW, while the LR samples were mainly clustered into Anaerostipes and Turicibacter enterotypes. Fig. 5Clustering and LDA analyses of gut microbiome data collected from Duroc [a], Landrace [b], and [c] pigs at three time points 73 days (T1), 123 days (T2), and 158 days (T3) of the feeding trial. [SEP] Justify:

Output: The text contrasts the claim by providing detailed information on the variations in microbiome composition among different breeds and time points of pigs. The data indicates that distinct enterotypes are characterized by different bacterial genera across breeds, suggesting that microbiome compositions are not identical and vary significantly among groups.


Input: Task: pass the claim using the given text. Claim: Astrobiological data from telescopes is analyzed using advanced statistical methods. [SEP] Text: dta", clear *get a sense of the distribution of observations per unit (i. e. , institution) in the/// panel dataset xtdes *run our fixed-effects regression model using the within regression// estimator (xtreg, with the fe option) qui: xtreg lneg lnstatea lntuition lntotfteiarep lnftfac lnptfac fe *run the Pesaran test and the Friedman test. xtcsd, pesaran xtcsd, friedman *test is also conducted. xtcsd, frees *We download the most recent version of xtcd (Eberhardt 2011). ssc install xtcd, replace *Then we run the test on variables of interest from the same panel dataset. xtcd lneg lntuition lnftfac lnptfac *Using the variables that we included in a fixed-effects model above, we// employ a random-effects *regression model and apply the test to the residuals. [SEP] Justify:

Output: The text appears to discuss statistical methods and tests, such as fixed-effects regression and the Pesaran test, but it does not specifically mention astrobiological data or telescopes. Therefore, it does not provide relevant information to support or contradict the claim.


Input: Task: corroborate the claim using the given text. Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: 1017/S0376892920000235 [Opens in a new window] This is an 4. 0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © The Author(s), 2020. Published by Press on behalf of Foundation for alarming number of wildlife species and parts are traded globally, threatening conservation efforts (Wilson-Wilson-Wilde2010). For example, 20 tonnes of pangolins and their parts are trafficked internationally annually, and c. 1 000 000 pangolins have been poached in the last decade (n. d. a). Populations of black rhino have been decimated from c. 70 000 in 1970 to just 2410 in 1995, resulting in the species now being classified as Critically Endangered. Growing demand has intensified the numbers of threatened, vulnerable and endangered animal species (Butchart et al. and Almond2010). The illegal wildlife trade has also increased the risk of pathogens, raising concerns about health security and infectious disease spread among humans (Daszak et al. Reference Daszak, Cunningham and Hyatt2000). Despite these dual risks to animal and public health, an estimated US$19 billion continues to be generated by this criminal enterprise yearly (Integrity 2011). With the rapid rise of information and communication technology, virtual marketplaces on the Internet are now conduits for the distribution, trafficking and sale of wildlife products (Rosen & Rosen and Smith2010, Herrel & Van der Herrel and Van der Meijden2014, Guan & Guan and Xu2015). China has been identified as a primary consumer destination, especially for ivory and rhino horn (Guan & Guan and Xu2015, n. d. b). In 2017, the revised law of the People’s Republic of China on the Protection of Wildlife came into effect and, combined with the efforts of the Coalition to Trafficking Online, the number of new wildlife product advertisements has reportedly declined (Xin & Xin and Xiao2019). However, wildlife product trading continues among Chinese nationals and Chinese-language speakers in other countries, including a rise in social media-based marketing and sale (Xiao & Xiao and Wang2015, Xiao et al. Reference Xiao, Guan and Xu2017, Xin & Xin and Xiao2019). Popular platforms include those operated in China (e. g. [SEP] Justify:

Output: The text corroborates the claim by discussing the extensive illegal wildlife trade that threatens conservation efforts and the population of endangered species. It highlights the impact of wildlife trafficking, including the poaching of pangolins and the decimation of black rhinos, which underscores the need for conservation organizations to address these issues. It also mentions the efforts to combat wildlife trafficking, such as the revised law in China and the Coalition to Trafficking Online, indicating active initiatives by conservation organizations.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: (Above) Genera that differentiate enterotypes at each time point. Bar length represents a log10-transformed linear discriminant score. Color corresponds to the enterotype in which a genus was found to be most abundant. (Below) Enterotype clusters identified using Calinski–Harabasz (CH) index maximizationFull size imageASV differentially represented in the Duroc, Landrace, and breedsAnalyses of ASV representation performed at each time point revealed the gut microbiomes of DR, LR, and LW pigs to be distinct (S1). When ASV abundances in DR pigs were compared to the average of those in LR and LW pigs at 73, 123, and 158 days, a total of 441, 401, and 324 ASV were found to be significantly different in terms of their representation (false discovery rate (FDR), 5%). Of these, 261 ASV classified as Firmicutes (177), Bacteroidetes (55), Proteobacteria (12), Spirochaetes (11), Actinobacteria (3), Chlamydiae (1), Fusobacteria (1), and Tenericutes (1) were shared between 73 and 123 days; 267 ASV classified as Firmicutes (180), Bacteroidetes (59), Proteobacteria (11), Spirochaetes (10), Actinobacteria (5), Fusobacteria (1), and Tenericutes (1) were shared between 123 and 158 days, and 191 ASV classified as phylum Firmicutes (128), Bacteroidetes (41), Proteobacteria (9), Spirochaetes (8), Actinobacteria (3), Fusobacteria (1), and Tenericutes (1) were shared between 73 and 158 days. The relative abundances of 184, 153, and 123 ASV were significantly different between LR versus LW pigs at 73, 123, and 158 days, respectively. Of these, 56 consistently separated LR from LW pigs at 73 and 123 days, specifically, Firmicutes (24), Bacteroidetes (16), Proteobacteria (9), Spirochaetes (3), Actinobacteria (3), and Fusobacteria (1). There were 38 ASV that were systematically different between LR and LW at 123 and 158 days. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that gut microbiomes of different pig breeds (Duroc, Landrace, and LW) are distinct. It details various ASV (Amplicon Sequence Variants) that were found to be significantly different in representation among the breeds over specific time points, which indicates considerable variation in microbiome composition. For example, it mentions that a total of 441, 401, and 324 ASV were significantly different, implying that microbiome compositions are not identical between individuals, even within closely related breeds.


Input: Task: pass the claim using the given text. Claim: Astrobiological data from telescopes is analyzed using advanced statistical methods. [SEP] Text: qui xtreg lneg lnstatea lntuition lntotfteiarep lnftfac lnptfac, re predict ue_residuals_re, ue *install the xtcd2 routine ssc install xtcd2, replace *check for weak cross-sectional dependence qui: xtreg lneg lnstatea lntuition lntotfteiarep lnftfac lnptfac, fe xtcd2 *use xtcdf (Wursten 2017) to allow for a much faster estimation of the// Pesaran cross-sectional *dependence test and provide additional statistics ssc install xtcdf, replace qui xtreg lneg lnstatea lntuition lntotfteiarep lnftfac lnptfac, fe predict ue_residuals_fe, ue xtcdf lneg lnstatea lntuition lntotfteiarep lnftfac lnptfac ue_residuals_fe * -Account// install routine by Hoechle (2018) of regression model with// Driscoll and Kraay (D-K) standard errors for use in Stata ssc install xtscc, replace. *run fixed-effects regression model with D-K standard errors and 2 lags of the AR term xtscc lneg lnstatea lntuition lntotfteiarep lnftfac lnptfac, fe lag(2) *check for cross-sectional dependence in the residuals of the regression, including// year fixed-effects qui xtscc lneg lnstatea lntuition lntotfteiarep lnftfac lnptfac i. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses technical details about statistical routines and methods in a specific statistical software (Stata) without any direct reference or connection to astrobiological data or telescopes.


Input: Task: corroborate the claim using the given text. Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: Future studies should develop more robust approaches to accurately confirming geolocation metadata. Despite uncertainty regarding the exact geolocation of users and the lack of Facebook access for Chinese nationals, there are large numbers of Chinese speakers residing outside of mainland China who may engage in online wildlife sale and purchasing. There is also the possibility that traders or consumers in China are using VPN services to participate in this trade. This activity occurs openly despite Facebook’s policy expressly prohibiting it. Facebook’s ‘Commerce policy section 3 – 4 Animals’ specifically prohibits the sale of “any product or part … [from] endangered or threatened animals; live animals, livestock, pets, and prohibited animal parts, including but not limited to bone, teeth, horn, ivory, taxidermy, organs, external limbs, secretions, or carcasses on Facebook and Instagram” (Policies n. d. ). Our results indicate that despite these prohibitions, a variety of wildlife products (including those of endangered and threatened species) are being offered for sale on the platform. Although we were not able to purchase or test the purported wildlife products in order to verify their authenticity, we used visual assessment of images in combination with text analysis. However, it is possible that not all products were authentic wildlife products, and some offers may have been scams. Future studies should develop additional verification approaches such as using deep learning for image classification and assessing other product-specific features in order to confirm whether the products are authentic. Furthermore, according to Facebook’s official help centre page, the company also states that they do not allow organizations that engage in terrorism or organized crime to be on the platform and will remove content that expresses support for groups involved in violent or criminal behaviour (Center n. d. ). However, organizations including the UN Office on Drugs and and have provided clear evidence of the link between illegal wildlife trade and professional criminal organizations (UNODC 2016, 2019). Hence, our findings provide clear evidence that Facebook’s self-regulation of this content, which likely violates applicable national law, international treaties and the platform’s own policies, is insufficient and represents a policy failure in the context of combatting online wildlife trafficking directly enabled by the platform. Other investigations have also uncovered wildlife trafficking on Facebook (Enano2019). an international conservation non-profit organization, found 2245 advertisements in Tagalog and English associated with the live reptile trade on Facebook groups in the Philippines (Sy2018). [SEP] Justify:

Output: The text corroborates the claim by discussing how organizations, such as the UN Office on Drugs and Crime (UNODC), have provided clear evidence of the link between illegal wildlife trade and organized criminal organizations. It outlines that despite Facebook's policies prohibiting the sale of wildlife products, illegal activities still occur on the platform, highlighting a failure in policy that conservation organizations aim to address. Additionally, reference to investigations by international conservation non-profit organizations supports the idea that conservation entities are actively involved in combating wildlife trafficking.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: These belonged to different phyla such as Firmicutes (24), Bacteroidetes (5), Proteobacteria (5), Actinobacteria (3), and Spirochaetes (1). Thirteen ASV classified as Firmicutes (5), Bacteroidetes (4), Proteobacteria (3), and Actinobacteria (1) were shared between 73 and 158 days. The significant ASV in the orthogonal contrasts of DR versus the average of LR and LW and LR versus LW at all three time points are presented as volcano plots (Fig. 6a–c), while the relative abundance is reported in Additional file 2. A comparison of the Duroc sire line with the combined LR and LW maternal lines at 73 days revealed a higher abundance of 6 Firmicutes. The most significant of these were classified as Ruminococcus and Clostridium. In a comparison of the two maternal lines with one another at 73 days, LR had higher representation of Bacteroidetes and Spirochaetes than LW. Within these phyla, the most significant genera were Prevotella, Bacteroides, and Treponema. Landrace also had a lower proportion of several ASV than LW at 73 days, many of which were classified as Clostridium, Campylobacter, Blautia, Eubacterium, Lactobacillus, and Roseburia (Fig. 6a). Fig. 6ASV with significantly higher or lower relative abundance when comparing the Duroc (DR) sire line vs the average of the Landrace and maternal lines (DR vs (LR+LW)/2) (left panel) or the two maternal lines (LR vs LW) (right panel) at three time points (73 days (T1) [a], 123 days (T2) [b], and 158 days (T3) [c]). Each volcano plot simultaneously indicates the log2 fold change (x-axis) and the –log10(p value) (y-axis) for each ASV plotted. Dashed lines indicate arbitrary thresholds of logFC and p value. Each point represents an ASV, annotation highlight the genus of the ASV, while color represents the phylum. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that different phyla and genera of microbiomes exist among individuals. It illustrates significant differences in microbiome composition, noting that certain phyla like Firmicutes, Bacteroidetes, and others vary among subjects, with specific genera being more abundant in particular lines of subjects over time. This suggests that microbiome compositions are not identical for everyone.


Input: Task: pass the claim using the given text. Claim: Astrobiological data from telescopes is analyzed using advanced statistical methods. [SEP] Text: endyear, fe lag(2) predict xtscc_residuals_fe2y, resid xtcdf xtscc_residuals_fe2y *Compare the estimated coefficients of interest to policy analysts of// researchers, by running and storing the results of three// regression models: (1) a fixed-effects model without *year fixed-effects (2) a fixed-effects model with year fixed-effects// and (3) a fixed-effects *model with year fixed-effects and D-K standard errors. eststo: qui xtreg lneg lnstatea lntuition lntotfteiarep lnftfac lnptfac, fe eststo: qui xtreg lneg lnstatea lntuition lntotfteiarep lnftfac lnptfac i. endyear, fe eststo: qui xtscc lneg lnstatea lntuition lntotfteiarep lnftfac lnptfac i. endyear, fe lag(2) *Use esttab command (with the label, p[(fmt)], and keep options as well as the// Estout varwidth option) to create a table of the stored regression results to// compare the estimated beta coefficients of variables of interest// across the three models esttab, label keep(lnstatea lntuition lntotfteiarep lnftfac lnptfac) varwidth(30) beta(%8. 3f) *end Rights and permissionsReprints and permissions Copyright information© 2021 this 10. 1007/978-3-030-60831-6_8Download citation. RIS. ENW. : 15 May 2021 Publisher Name: : 978-3-030-60830-9 Online ISBN: 978-3-030-60831-6eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)Share this 34. 9910 units per month Download 29. 95 Price excludes VAT (USA) Available as PDF Read on any device Instant download Own it forever Buy 59. 99 Price excludes VAT (USA) Available as EPUB and PDF Read on any device Instant download Own it forever Buy e79. [SEP] Justify:

Output: The text does not provide information relevant to the claim. It discusses the use of regression models and statistical analysis in a way that does not specifically mention astrobiology or telescope data analysis. Thus, it is deemed irrelevant (cold) to the claim made.


Input: Task: corroborate the claim using the given text. Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: CNN found that cheetahs were being offered for sale on social media platforms including YouTube and Instagram (owned by Facebook), including from Gulf nation countries in Arabic language, while the of have collaborated on uncovering illegal wildlife trafficking of pangolins (Böhler Reference Böhler2019, Formanek et al. Reference Formanek, Karadsheh and Qiblawi2019). Our results provide additional evidence of the specific ways by which Facebook enables user engagement to facilitate sales between wildlife sellers and buyers. From a methodological perspective, a growing number of studies have utilized data mining and machine learning to classify illegal wildlife product sales (Hernandez-Castro & Hernandez-Castro and Roberts2015, Harrison et al. Reference Harrison, Roberts and Castro2016, Austen et al. Reference Austen, Bindemann, Griffiths and Roberts2018, et al. Minin, Fink, Tenkanen and Hiippala2018, Parham et al. and Berger-Wolf2018). Our study used web scraping to simulate user searches on Facebook both retrospectively and prospectively. This approach can be replicated for the further detection of wildlife trafficking in other languages on Facebook and other SNS platforms and be adapted for the detection of other online criminal activity. Manually annotated results can also be used to develop machine learning classifiers for future automated detection of Chinese wildlife marketing and sales content. Text analysis is an important part of ensuring the accurate identification and characterization of wildlife sales posts and identifying changes in selling and code word strategies. Based on our findings, it appears that SNS users are starting to use emojis and new code words instead of species-related keywords (Alfino & Alfino and Roberts2018). This change could increase the amount of false-negative posts when using machine learning and emphasizes the need for continued manual annotation to uncover new trends in the detection of covert activities. For example, some posts only contained text or generic terms, but did not mention specific animals. This may specifically impact the accuracy of algorithms in detecting content, particularly if it is created in non-English characters. Although we did not use machine learning approaches to content code, based on these results, we plan to develop a Chinese-language classifier to detect illicit online sales of wildlife products. Furthermore, we observed that many wildlife selling interactions are initiated on Facebook, but have their transactions finalized in private messaging. [SEP] Justify:

Output: The text corroborates the claim by detailing efforts to combat illegal wildlife trafficking through social media by analyzing how wildlife sales occur online, specifically on platforms like Facebook. It discusses how organizations have collaborated to uncover illegal wildlife trafficking of species such as pangolins, emphasizing the role of technology and digital methods in fighting these illegal activities, which is aligned with the work of conservation organizations.


Input: Task: contrast the claim using the given text. Claim: Everyone's microbiome composition is identical. [SEP] Text: Full size imageSignificant differences were also observed at 123 days when comparing ASV abundances in DR pigs with the average of LR and LW. Duroc pigs had higher proportion of 8 Firmicutes, 1 Actinobacteria, and 1 Bacteroidetes than the two maternal lines. The most abundant genera within these phyla were Catenibacterium, Prevotella, and Collinsella. On the other hand, DR, compared to the average of LR and LW at 123 days had a lower proportion of 8 Firmicutes, 4 Bacteroidetes, and 2 Spirochaetes. Within these phyla, the most significant genera were classified as Clostridium, Prevotella, and Treponema. Remarkable differences in ASV abundances were also observed when comparing LR to LW at 123 days. Specifically, LR had a higher proportion of 6 Bacteroidetes, 6 Firmicutes, 2 Proteobacteria, and 1 Spirochaetes. Within these phyla, the predominant genera were Anaerococcus, Barnesiella, Clostridium, and Prevotella. Interestingly, the gut microbiome of LR at 123 days also had a lower proportion of ASV belonging to Clostridium and Eubacterium than LW (Fig. 6b). The orthogonal contrast between paternal and maternal lines at 158 days revealed a higher abundance of 13 Firmicutes, 2 Bacteroidetes, and 1 Actinobacteria. Within the Firmicutes phylum, as observed in 73 and 123 days, the most significant genera were Catenibacterium and Clostridium. The paternal line had a lower abundance of 5 Firmicutes and 1 Spirochaetes. Of these, one of the most significant genera was Turicibacter as observed in 73 and 123 days. We also discovered differences between the two maternal lines at 158 days. LR compared to LW had a particularly higher abundance of Bacteroides, Campylobacter, Coprococcus, Enterococcus, and Fusobacterium, as well as a lower abundance of Lactobacillus, Ruminococcus, and Desulfovibrio (Fig. 6c). Of these, the genus Bacteroides was the most significant discriminant of LR and LW across the three time points. [SEP] Justify:

Output: The text contrasts the claim by detailing significant differences in microbiome composition among different pig lines, including variations in the abundance of specific phyla and genera. It notes that Duroc pigs exhibit higher proportions of certain bacteria compared to LR and LW pigs, and that LR and LW also have notable differences in their microbiomes. This suggests that microbiome compositions vary greatly among individuals rather than being identical.


Input: Task: pass the claim using the given text. Claim: Restoration of mangrove forests enhances coastal resilience against climate change. [SEP] Text: Responses to Sea-Level ChangeMean sea levels are currently rising as a result of both thermal expansion of ocean water due to increasing temperatures and the melting of polar and land ice [3]. The rise in sea level is not globally uniform as a result of water buildups in some areas, differences in water density due to varying sea temperatures, and the weight of ice sheets. Over geological time, as sea level has waxed and waned in relation to alternating periods of glaciation and warming, mangroves have had to adjust (or not) to rises and falls in sea level. Adjustments in Relation to Sea LevelA number of local and regional short- and long-term processes affect mangrove surface elevation relative to sea level. Eustatic changes in sea level, isostatic changes, subsidence, and changes in the subsurface volume of fluids (e. g. , oil, water) are the major long-term processes; short-term processes include accretion of fine sediment p9. 2 cal kp BP (Fig. 1). However, very abrupt changes in the vertical pattern of peat and carbonate deposits suggest that the mangroves drowned fairly quickly (Fig. 1), supporting other evidence of rapid sea-level change about 9–11 kp BP [12]. Buried mangrove peats lie in other coastal waters, such in the Caribbean [13] and the northern [23]. And while the thicknesses of these peat deposits indicate that mangroves were able to keep pace with the rise in sea level for long time periods, the rate of sea level increased beyond a critical threshold at which mangroves were not able to keep up [8•]. Fig. 1Photograph of a 1. 5-m-long sediment core taken in 22 m water depth at the group (Latitude 17° 13. 5′ S, Longitude 146° 05′ E) on the in February 2003. Note the abrupt demarcation halfway up the core between brown mangrove peat and more recent fine carbonate gray sand. Mangrove peat on the shelf is typically aged at 9. 3–9. [SEP] Justify:

Output: The text discusses the effects of sea-level change on mangroves but does not directly address the claim regarding the enhancement of coastal resilience through the restoration of mangrove forests. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Conservation organizations often work to combat illegal wildlife trafficking. [SEP] Text: Virtual marketplaces that leverage the Internet’s accessibility and anonymity are conduits for the distribution, trafficking and sale of a variety of prohibited services, products and commodities, including illicit drug trafficking, antiquities, human trafficking and illegal wildlife trafficking (Rosen & Rosen and Smith2010, Latonero2011, Campbell2013, Mackey & Mackey and Liang2013, Herrel & Van der Herrel and Van der Meijden2014, Guan & Guan and Xu2015, Mendel & Mendel and Sharapov2016, Mackey et al. Reference Mackey, Kalyanam, Katsuki and Lanckriet2017). Combating illegal online SNS wildlife sales is a race against time with criminal actors. By using big data and advanced data mining approaches, rapid detection of wildlife-related cybercrime is possible, particularly when leveraging computational methods of natural language processing, machine learning and deep learning informed by in-depth manual annotation to characterize content and interactions between sellers and buyers (Mackey et al. Reference Mackey, Kalyanam, Katsuki and Lanckriet2017, Li et al. Reference Li, Xu, Shah and Mackey2019, Xu et al. Reference Xu, Li, Cai and Mackey2019). Our study represent one phase of this solution through developing an approach to collecting data from one of the world’s most popular SNS platforms by simulating user searches and developing a manually labelled dataset that can be used for training in a machine learning inference phase. Importantly, this methodology can be used to eventually develop surveillance that occurs close to real time in order to detect, classify and report illegal wildlife posts for action by platforms in conjunction with law enforcement and conservation groups, as has been pursued for illicit online drug sales (Mackey et al. Reference Mackey, Kalyanam, Klugman, Kuzmenko and Gupta2018). Such solutions are crucial in the face of an evolving and growing wildlife cybercrime landscape. However, these efforts need to be supported with meaningful collaboration from international organizations, government agencies, technology companies, academia and non-governmental organizations and a decrease in demand for these products from the general public. Coordination of efforts to address Internet and SNS-based wildlife trafficking should start with more robust surveillance in multiple languages, including incorporating localized keywords and vocabulary specific to wildlife trade, while also being adaptive to changes in selling strategies and language. [SEP] Justify:

Output: The text corroborates the claim by detailing how conservation groups are involved in combating illegal wildlife trafficking using various strategies, including advanced technology and collaboration with law enforcement and other organizations. It specifically mentions 'action by platforms in conjunction with law enforcement and conservation groups,' indicating that conservation organizations play an active role in addressing and combating illegal wildlife trafficking.