dataesr/scientific-paragraphs-categorization huggingface dataset:
| RESULTS The isotopic data are presented on Table  2  and summarized on   Table 3. The 87 Sr/ 86 Sr data are not normally distributed (W = 0. 575, p < 0. 001), while the δ 13 C (W = 0. 961, p = 0. 515) and δ 18 O VPDB (W = 0. 967, p = 0. 645) data are normally distributed. Strontium ratios range from 0. 70619-0. 70845, with a median value of 0. 70815 (IQR 0. 00014). Nineteen of the 22 individuals  Using the bagplot to identify statistical outliers using both 87 Sr/ 86 T A B L E 2 Strontium, Sr and δ 18 O (Figure  5 ), the two outliers with low 87 Sr/ 86 Sr remain as statistical outliers but the individual with the highest 87 Sr/ 86 Sr is within the loop polygon and not an outlier. One individual is an outlier along the δ 18 O axis, the individual with the highest δ 18 O value within the dataset.
, 2003 (Bentley et al. , , 2004;; Price et al. , 2002; Stantis et al. , 2016). This method, however, has often been considered less satisfactory as the mean might not represent the local biosphere and two standard deviations (estimated to cover 95% of a given dataset) might encompass non-local values as well as local individuals. This issue also applies to bagplot analysis, although the use of median and IQR is preferable to mean and standard deviation with non-normally distributed data, which archeologically derived isotopic data often is  (Price et al. , 2002 (Price et al. , , 2008)). Given the geographical spread and great time-depth of occupation in Jordan, isotopic studies of paleomobility are still nascent in the region. Alakkam (2002)   (Harrison, 1997). Whether due to invasion/ migration, rapid climate change, economic pressures, or other social and environmental changes, the transition from EBA-MBA is estimated to be marked by increasing sedentism and smaller spheres of interaction  (Langgut et al.
Biodistance information has been lost due to material limitations in the assemblage, but the available dental traits suggest diverse ancestral background for the occupants of Tomb 62. Future research statistically integrating the two types of data are warranted, especially in sites where preservation is better and making use of the entirety of the ASUDAS traits, feasible in noncommingled burials. ACKNOWLEDGMENTS Thanks to Dr. Sam Rennie for sitting down for a whole Powerpoint on Zoom when all conferences were canceled, just so the first author could talk through the results with an "audience. " Joanne Peterkin was invaluable in helping prepare the carbonate samples. Funding to carry out this research was provided by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (H2020 European Research Council, grant agreement No 668640).
Article analysed with the genomes of selected populations-including Papua New Guinean Highlanders and Bismarck Islanders  16, 18, 19  -and archaic hominins  [20] [21] [22]  (Supplementary Note 2 and Supplementary Table  1 ). The final dataset involves 462 unrelated individuals, including 355 individuals from the Pacific region, and 35,870,981 single-nucleotide polymorphisms (SNPs) (Fig. 1b ). Using ADMIXTURE, principal component analysis (PCA) and a measure of genetic distance (F ST ), we found that population variation is explained by four components, associated with (1) East and Southeast Asian individuals; (2) Papua New Guinean Highlanders; (3) Bismarck Islanders, Solomon Islanders and ni-Vanuatu; and (4) Polynesian outliers (here 'Polynesian individuals') (Fig. 1c, d , Extended Data Fig.
Latitude Atayal (10)   Agta (20) Paiwan (9)   Cebuano (20)   Vella Lavella (18)   Malaita (18)   Rennell (  4 ) Bellona (15)   Santa Cruz (14)   Tikopia (10)   Ureparapara (20)  Ambae (15)  Maewo (8)   Emae (15)   Santo (20)   Malakula (20)   Efate (21)  Tanna (20)   Latitude   19  , Vernot et al. 16  and Malaspinas et al. 18  , respectively. b, The number of SNPs (left), expressed in tens of millions, and comparison with dbSNP (right). New variants are SNPs that are absent from available datasets  16, 18, 19  and dbSNP. c, ADMIXTURE ancestry proportions at K = 6 (lowest cross-validation error; for all K values, see Extended Data Fig. 1 ). ADMIXTURE results for Australian populations are discussed in Supplementary Note 3. d, PCA of Pacific Islanders and East Asian individuals. The proportion of variance explained is indicated in parentheses.
e, Population levels of heterozygosity (for all populations, see Supplementary Fig. 9 ). Population samples were randomly down-sampled to obtain equal sizes (n = 5). The line, box, whiskers and points indicate the median, interquartile range, 1. 5× the interquartile range and outliers, respectively. a, c, Maps were generated using the maps R package  51. We then incorporated western Remote Oceanian populations into the model, represented by ni-Vanuatu individuals from Malakula. We estimated that the ancestors of ni-Vanuatu individuals received migrants from the Bismarck that contributed more than 31% of their gene pool (95% confidence interval, 31-48%) less than 3 ka (Extended Data Fig. 2b  and Supplementary Table  5 ), which is consistent with ancient DNA results  [8] [9] [10]. However, the best-fitted model revealed that the Papuan-related population who entered Vanuatu less than 3 ka was a mixture of other Near Oceanian sources  8, 23  : the Papuan-related ancestors of ni-Vanuatu diverged from Papua New Guinean Highlanders and later received approximately 24% (95% confidence interval, 14-41%) of Solomon Islander-related lineages.
We also detected a strong signal around OSBPL10, associated with dyslipidaemia and triglyceride levels  41  and protection against dengue  42  , which we found to have been adaptively introgressed from Neanderthals (Extended Data Fig. 7 ). Population-specific signals included LHFPL2 in Polynesian individuals (Extended Data Fig. 8c, d ), variation in which is associated with eye macula thickness-a highly variable trait involved in sharp vision  43. LHFPL2 variants reach around 80% frequency in Polynesian individuals, but are absent from databases, highlighting the need to characterize genomic variation in understudied populations. Because most adaptive traits are expected to be polygenic  44  , we tested for directional selection of 25 complex traits with a well-studied genetic architecture  45  , by comparing the integrated haplotype scores (iHS) of trait-associated alleles to those of matched, random SNPs  46.
Our analyses also provide insights into the settlement of Remote Oceania. Ancient DNA studies have proposed that Papuan-related peoples expanded to Vanuatu shortly after the initial settlement, replacing local Lapita groups  8, 10, 23. We suggest that most East-Asian-related ancestry in modern ni-Vanuatu individuals results from gene flow from admixed Near Oceanian populations, rather than from the early Lapita settlers. These results, combined with evidence of back migrations from Polynesia  6, 10, 23  , support a scenario of repeated population movements in the Vanuatu region. Given that we explored a relatively limited number of models, archaeological, morphometric and palaeogenomic studies are required to elucidate the complex peopling history of the region. The recovery of diverse Denisovan-introgressed material in our dataset, together with previous studies  28, 29  , shows that modern humans received multiple pulses from different Denisovan-related groups (Extended Data Fig.
Whole-genome sequencing data Whole-genome sequencing was performed on the 317 individual samples (Supplementary Table  1 ), with the TruSeq DNA PCR-Free or Nano Library Preparation kits (Illumina). After quality control, qualified libraries were sequenced on a HiSeq X5 Illumina platform to obtain paired-end 150-bp reads with an average sequencing depth of 30× per sample. FASTQ files were converted to unmapped BAM files (uBAM), read groups were added and Illumina adapters were tagged with Picard Tools version 2. 8. 1 (http://broadinstitute. github. io/picard/). Read pairs were mapped onto the human reference genome (hs37d5), with the 'mem' algorithm from Burrows-Wheeler Aligner v. 0. 7. 13  52  and duplicates were marked with Picard Tools. Base quality scores were recalibrated with the Genomic Analysis ToolKit (GATK) software v.
3. 8  53. Whole-genome data for Bismarck Islanders  16  were processed in the same manner as the newly generated genomes, while for Papua New Guinean Highlanders  18  and other populations of interest  19  , raw BAM files were converted into uBAM files, and processed as described above. Variant calling was performed following the GATK best-practice recommendations  54. All samples were genotyped individually with 'Haplo-typeCaller' in gvcf mode. The raw multisample VCF was then generated with the 'GenotypeGVCFs' tool. Using BCFtools v. 1. 8 (http://www. htslib. org/), we applied different hard quality filters on invariant and variant sites, based on coverage depth, genotype quality, Hardy-Weinberg equilibrium and genotype missingness (Supplementary Note 2). The sequencing quality was assessed by several statistics (that is, breadth of coverage 10×, transition/transversion ratio and per-sample missingness) computed with GATK  54  and BCFtools.
We also compared observed and simulated F ST values, computed with vcftools v. 0. 1. 13  70 , for all population pairs. We checked that parameter estimates were not affected by background selection and biased gene conversion (Supplementary Note 4). We calculated confidence intervals with a nonparametric block bootstrap approach; we generated 100 bootstrapped datasets by randomly sampling with replacement the same number of 1-Mb blocks of concatenated genomic regions as were present in the observed data. For each bootstrapped dataset, we obtained multi-SFS with Arlequin v. 3. 5. 2. 2  71  and re-estimated parameters with the same settings as for the observed dataset, with 20 replicate runs. Finally, to obtain the 95% confidence intervals, we calculated the 2. 5% and 97. 5% percentile of the estimate distribution obtained by nonparametric bootstrapping.
We evaluated the accuracy of demographic parameter estimation, using a parametric bootstrap approach. We simulated, with fastsim-coal2  24  , x 1-Mb DNA loci, with x chosen to obtain the same numbers of segregating SNPs and monomorphic sites as in the observed data, assuming parameters maximizing the likelihood under each model. We then generated 20 simulated SFS by random sampling and used bootstrapped SFS to re-estimate parameters under the same settings as for the original dataset (65 expectation conditional maximization cycles, 600,000 simulations and 100 runs per simulated SFS). We calculated the mean, median and the 2. 5% and 97. 5% percentiles of the distribution of parameter estimates obtained by parametric bootstrapping, and checked that they included the true (simulated) parameter value.
Admixture models We applied two ABC approaches  72  to test for different admixture models for Near Oceanian populations and estimated parameters under the most probable model. Model choice and posterior parameter estimation by ABC are based on summary statistics  73. The first approach, developed in the MetHis method  74  , is based on the moments of the distribution of admixture proportions and explicit forward-in-time simulations that follow a general mechanistic admixture model  75. The second approach uses-as summary statistics-the moments of the distribution of the length of admixture tracts  76, 77. We assumed three competing models of admixture: a single-pulse, a two-pulse or a constant-recurring model (Supplementary Notes 5, 6). We checked a priori the goodness-of-fit of simulated and observed statistics with the gfit function implemented in the abc R package  78.
For the best scenario identified, we conducted an additional 20,000 simulations with MetHis. We then used all 30,000 simulations computed under the winning scenario for joint posterior parameter estimation, with the neural-network ABC approach implemented in the abc R package  78. The performance of the method is described in Supplementary Note 5. For the approach based on admixture tract length, we performedunder each alternative admixture model-5,000 simulations of 100 5-Mb linked DNA loci with fastsimcoal2  24  , assuming a variable recombination rate sampled from the 1000 Genomes Project phase 3 genetic map  62. We performed 10,000 additional simulations for parameter estimation under the winning model. As summary statistics, we used the mean and variance, across the 100 5-Mb regions, of the mean, minimum and maximum of the distribution of the length of admixture tracts across Near Oceanian populations.
The six resulting summary statistics were computed based on local ancestry inference, with RFMix v. 1. 5. 4  80  , which was run with three expectation-maximization steps, a window of 0. 03 cM, and Taiwanese Indigenous peoples and Papua New Guinean Highlanders as source populations. The performance of the method is described in Supplementary Note 6. We used the logistic multinomial regression and the neural-network ABC methods implemented in the abc R package  78  for model choice and parameter estimation, respectively.
Archaic introgression Before performing archaic introgression analyses, we masked our whole-genome sequencing dataset for regions non-accessible in archaic genomes. We merged the masked dataset with the high-coverage genomes of Vindija and Altai Neanderthals and the Altai Denisovan  [20] [21] [22]. We assessed introgression between archaic hominins and modern humans with D-statistics  81. We computed a D-statistic of the form D(X, West Eurasians/East Asians/Africans; Neanderthal Vindija, chimpanzee) and D(X, West Eurasians/East Asians/Africans; Neanderthal Vindija, Denisova Altai) to test for introgression from Neanderthal; and D-statistics of the form D(X, West Eurasians/East Asians; Denisova Altai, chimpanzee) and D(X, West Eurasians/East Asians; Denisova Altai, Neanderthal Vindija) to test introgression from Denisovans.
The last two D-statistics were used to account for the more-recent common ancestor between Neanderthals and Denisovans. We computed f 4 -ratios to estimate the proportion of genome-wide Neanderthal and Denisovan introgression in a modern human population (Supplementary Note 7). All D-and f 4 -ratio statistics were computed with 'qpDstat' and 'qpF4ratio' implemented in ADMIXTOOLS v. 5. 1. 1  81. A weighted-block jackknife procedure dropping 5-cM blocks of the genome in each run was used to compute standard errors. We used two statistical methods to identify archaic sequences in modern human genomes. The first, S-prime (S′), identifies introgressed sequences without the use of an archaic reference genome  28. For the identification of S′ introgressed segments in Pacific genomes, we only considered variants with a frequency less than 1% in African individuals from the Simons Genome Diversity Project (SGDP) dataset  19  , and segments were detected in each population separately.
Classic sweeps For the detection of classic sweep signals, we combined the interpopulation locus-specific branch lengths (LSBL)  94  and cross-population extended haplotype homozygosity (XP-EHH)  95  statistics into a Fisher's score (F CS ). We estimated the F CS as the sum of the -log 10 (percentile rank of the statistic for a given SNP) of all statistics, and defined 'outlier SNPs' as those with a F CS among the 1% highest genome-wide. Putatively selected regions were defined as genomic windows with a proportion of outlier SNPs within the 1% highest genome-wide, after partitioning all windows into five bins based on the number of SNPs. The test, reference and outgroup populations used are described in Supplementary Note 16. LSBL and XP-EHH statistics were computed with the optimized, window-based algorithms implemented in selink (https:// github.
Polygenic adaptation We searched for evidence of polygenic adaptation, using an approach testing whether the mean integrated haplotype score (iHS) of trait-increasing alleles differed significantly from that of random SNPs with a similar allele frequency  46, 96. We obtained GWAS summary statistics for 25 candidate complex traits from the UK Biobank database  45  , including traits relating to morphology, metabolism and immunity, as these phenotypic traits are strong candidates for responses, through natural selection, to changes in climatic, nutritional and pathogenic environments. We classified SNPs as 'trait-increasing' or 'trait-decreasing' based on UK Biobank effect size (β) estimates. We computed iHS with selink, for each SNP and population, and standardized scores in 100 bins of DAF.
2. b, Maximum-likelihood demographic models for western Remote Oceanian individuals (VAN). The likelihoods of the two models are not considered to be different. Point estimates of parameters and 95% confidence intervals are shown in Supplementary  Article Pie charts indicate the derived allele frequency in purple, in which the radius is proportional to the sample size (Supplementary Table  1 ). The pie charts for the populations of Santa Cruz and Vanuatu were moved from their sampling locations for convenience. Maps were generated using the maps R package  51. !"#$ % & ' ( )" $#"$ *+ *+"#$ % & ' ( , $ !-#. " + /"$ # , " 0%1 %$ $. 2$ % #0 * 3 $ +4 4$ %1 5$ %"$1 1#*3 %67% 4. 2 $ #0$ # 4 0 $ 0+"$ " " 0+ $ !684 # $ % 4. "$ /"$ # , " 0%3 0 9 #: $ "3;3 0 "$ %: $ "3;3 0+%053 $ 6 -$ "$ $ 0 8"3 3 $ "$ $ 0"3""3 + 904.
Maps were generated using the maps R package 51. Extended Data Fig. 6 | Classic sweep signals detected in Papuan-related populations. a-d, Manhattan plots of classic sweep signals in Papua New Guinean Highlanders (a), Solomon Islanders (b), ni-Vanuatu (c) and Philippine Agta (d). a-d, The y axis shows the -log 10 (P value) for the number of outlier SNPs per window. Each point is a 100-kb window. The names of genes associated with windows with significant sweep signals are shown.
Acknowledgements We thank all volunteers and Indigenous communities participating in this research;  S. Créno  and the  HPC Core Facility of Institut Pasteur (Paris)  for the management of computational resources;  F. Mendoza de Leon Jr , NCCA chairperson 2010-2016, for his support;  C. Ebeo ,  O. Casel ,  K. Pullupul Hagada ,  D. Guilay ,  A. Manera  and  R. Quilang  of  Cagayan State University ,  Lahaina Sue Azarcon  and  Samuel Benigno  of  Quirino State University , and the regional and provincial offices of the  National Commission for Indigenous Peoples (NCIP)-Cagayan Valley  for their support and assistance. J. C. is supported by the  INCEPTION  programme  ANR-16-CONV-0005  and the  Ecole Doctorale FIRE-CRI-Programme Bettencourt  and L. R. A. by a  Pasteur-Roux-Cantarini fellowship.
Data availability The whole-genome sequencing dataset generated and analysed in this study is available from the European Genome-Phenome Archive (EGA; https://www. ebi. ac. uk/ega/), under accession code EGAS00001004540. Data access and use is restricted to academic research in population genetics, including research on population origins, ancestry and history. The SGDP genome data were retrieved from the EBI European Nucleotide Archive (accession codes PRJEB9586 and ERP010710). The genome data from Malaspinas et al. 18  were retrieved from the EGA (accession code EGAS00001001247). The genome data from Vernot et al. 16  were retrieved from dbGAP (accession code phs001085. v1. p1).
The different stance values are exemplified in examples (3) to  (10). Examples in French are taken from the interview data of the LANGSNAP corpus, while examples in English are taken from the PROLINGSA corpus 4  (Leclercq et al. 2021) , which is a longitudinal database consisting of a series of five interviews performed by two investigators (one French, the other American) with five French Erasmus students before, during, and after an academic stay abroad in a British or Irish university. 5 The transcription conventions adopted in all examples were taken from the CHAT transcription manual (MacWhinney 2000).
Use in English and French Conversation While "I think" has been widely described as one of the most frequent stance markers in English conversation, there is very little data on the use of je pense. In one of the few contrastive studies on opinion markers in French and English,  Mullan (2010, pp. 119-23)  claims that "I think" and je pense take on similar interactional functions, in fairly similar proportions. Based on a dataset of ten hours of French and English conversations, audio recorded in Australia between August 2000 and September 2002, Mullan found that two thirds of her occurrences were discourse markers with a pragmatic function, while the remaining occurrences were epistemic instances, mostly to express speaker opinion. She also suggests that "I think" is regularly used with evidential values, while she does not observe such use with je pense.
Regarding the use of "I think",  Salsbury and Bardovi-Harlig (2000) , in their paper on the acquisition of modality in L2 English, situated within an interlanguage pragmatics approach, presented a twelve month longitudinal study in which eight learners of English (all beginners at the onset of the project, and with a variety of different source languages) were enrolled in an intensive English program at Indiana University. The database consists of various types of recorded interactions: learner to learner and learner to native speakers. The authors analyzed instances of oppositional talks in conversation extracts. Their results highlight the fact that low-level and more advanced learners make a frequent use of "I think" and "maybe" to mitigate their disagreements in oppositional talk, rather than modal verbs.
Dataset We used data from the LANGSNAP project, which is a longitudinal study spanning over 21 months, including 9 months abroad, during which the researchers tracked the linguistic development of 29 learners of French. Our database includes 10 French native speakers and 26 Anglophone learners of French, with pre-test (PT) and Visit Abroad 3 (VA3) semi-guided interview data (we excluded participant 100, a highly proficient French/English bilingual, and participants 108 and 126, as their mother tongues (L1s) were Finnish and Spanish, respectively). All learner participants were adult students enrolled in a British university to study languages, with a special focus on French. As part of their undergraduate curriculum, they had to spend the third or fourth year of their programme abroad in a French-speaking country.
A statistical test to examine the relationship between group membership and the frequency of use of je pense was conducted in the three subgroups of the dataset (FrL1, FrL2 PT, and FrL2 VA3). Because this dataset was significantly skewed, we conducted a Kruskal-Wallis test in order to determine whether group membership was significantly related to the percentage of occurrences of je pense produced. The results showed a significant difference between the mean ranks of the FrL1 and FrL2PT groups-H(3) = 7. 896, p = 0. 01929. This suggests that at PT level, learners used je pense differently from native speakers, while percentage of use at VA3 shows no significant difference with either FrL1 (H(3) = 0. 605, p = 0. 4364) or FrL2PT (H(3) = 4. 1007, p = 0. 04287) groups. The fact that there is no significant difference between PT and VA3 suggests no change over time.
However, the fact that the learners are significantly different from the NSs at PT but not VA3 suggests some change. We will now have a closer look at the syntactic position and discursive functions that je pense takes on in the oral production of L1 and L2 French speakers. Table  2  presents the syntactic distribution of all occurrences of je pense in the dataset. It clearly shows that native speakers and learners, irrespective of the time spent abroad, favor its use as a matrix clause (je pense que). Native speakers' second choice is its use as an IC, either as a short answer, as in (  14 ), or as the main verb of a full IC. In those two categories (IC and MC), je pense features in initial position in the clause, sometimes after a "connective", as proposed by  Kärkkäinen (2003, p. 47 ) (see (  2 )).