dataesr/scientific-paragraphs-categorization huggingface dataset:
Unfortunately, we cannot ascertain the distribution of strong/weak negative concord systems in the AIS. In terms of frequency and probability, the data in Table  15  show that N1 is often retained when a negative quantifier is in preverbal subject position (although for this condition the ALF does not provide data for all datapoints). Furthermore, N1 is likely to occur with the adverb 'yet'. Unlike northern Italo-Romance (Section 3. 1), Gallo-Romance dialects do not exhibit any n-word for 'yet'. However, the incidence of N1 is relatively higher with 'yet' than in other negative clauses that contain N2 or postverbal NPIs. This may indicate that 'yet'/'still' is not entirely polarity-neutral. Eventually, I tested a mixed model with factors Region, NPI Type, and Clause. The model was tested on indicative and modal clauses, which occur in the ALF with and without NPIs (see Table  11 ).
In any case, the values of the probability index for the factor Clause (modal vs indicative) were always too close to reach any solid conclusion. The remainder of the section focuses on the occurrence of N1 in combination with N2 and a NPI. As we will see in Section 4. 2, the phenomenon I dubbed N2-doubling is marginally allowed in the ALF dataset only in two contexts: with the adverb plus 'anymore' and with the ni … ni coordination. As in the case of Italo-Romance, N2-doubling proved to be significant with respect to the retention/omission of N1 in various models. As predictable, Table  16  shows that N1 is disfavored in sentences displaying N2-doubling.
N2 (ALF) In the Gallo-Romance corpus, the incidence of N2 is at ceiling in almost all contexts, as shown in Table  17. The distribution of N2 in the ALF is quite homogeneous even if NPIs are introduced in the model. Cases of N2-doubling, in fact, are quite sporadic, although the data in Table  18  show some interesting tendencies that can be compared with those observed in the literature (see Section 5). In the ALF dataset, 'Anymore' and, to a lesser extent, negative coordinators are marginally found to co-occur with N2, whereas negative quantifiers (in subject position) and the adverb guère are in complementary distribution with N2. 'Yet' always cooccur with N2, thus confirming the intuition that the adverb encoding 'yet' is not a NPI, although the data on N1 (Section 4. 1) suggested that the 'yet'/'still' alternation is not entirely polarity-neutral.
Interim conclusions (ALF) Clause typing has some effect on the distribution of N1 in the ALF dataset: indicatives are the context in which N1 is less retained, but the ranking of the other clause environments is difficult to interpret in the light of current theorizing. N2, by contrast, is at ceiling in all clausal contexts. Regarding the effect of NPIs, N1 is retained more frequently in clauses containing a NPI, especially a preverbal one. N2 sporadically co-occurs with non-argumental NPIs. If a NPI and N2 co-occur, N1 is often, but not always dropped.
Combining datasets and reducing factors In doing my statistical analysis in Sections 3 and 4, I have maintained the data from the AIS and the ALF separated for practical and methodological reasons. Although they seem contiguous on maps (cf. Fig. 1  and Fig. 2 ), Gallo-and Italo-Romance systems are separated by the Alps and, therefore, it is reasonable to hypothesize that the conditions ruling the distribution of NI/N2 in the two areas are not necessarily alike. For the sake of clarity, Table  19  resumes the conclusions of Sections 3 and 4: Despite some difficulties and limitations, the preliminary observations made in Sections 3 and 4 allowed us to reach some provisional conclusions regarding both methodological and theoretical aspects. First and foremost, we proved that mixed models including grammatical factors always perform better than models that are entirely based on geographical factors.
Both type of factors, i. e. grammatical and geographical, can be improved and refined, but it seems reasonable to conclude that combined models fit the data best. Second, by examining the distribution of N1 and N2 in the two datasets, we noticed that the AIS and ALF are not homogeneous test beds, especially in the analysis of N1. Northwestern Italy provides no clear indication regarding the 'loss' of N1 as most AIS datapoints exhibit either N1 or N2, whereas the ALF is characterized by a relatively higher incidence of languages with discontinuous negation. Then, it seems to me that, to obtain better statistical results, the data from the AIS and the ALF should be merged together, rather than being compared. This holds particularly true for one group of factors (Clause Type) whose effects are not clear and not convergent in both Gallo-and Italo-Romance dialects (see Table  19 ).
Furthermore, to strengthen statistical results, we may also want to focus, deductively, only on those specific factors that, according to the recent literature, are expected to affect the incidence of N1 and N2. For instance, since veridicality is often mentioned as factor affecting negation marking, we can test whether the incidence of N1 and N2 varies significantly in two groups of clauses-indicative (declarative) clauses vs interrogatives-without testing the whole dataset. However, even if all other types of clauses are removed and data from both the AIS and ALF are merged together, I found that the distinction between indicative declaratives and interrogative was not significant with respect to N1 in a model with factors Clause and Region. Interestingly, however, clause proved statistically significant with respect to the distribution of N2 (0.
Negative concord is a case in point because the data from the AIS and ALF seem to converge (cf. Table  19 ), but both atlases provide us with incomplete indications. Conversely, by merging both, we may obtain a contingency table such as Table  21 , reporting the frequency of each pattern of negation (absence of negation, N1, N2, discontinuous negation) per type of NPI. N2 N1+N2 Anymore 0,29 0,59 0,07 0,05 Anywhere 0,22 0,42 0,29 0,07 Neither … nor 0,29 0,57 0,13 0,02 Anything (postV) 0,51 0,41 0,07 0,01 Never 0,48 0,47 0,05 0,01 Nobody (preV) 0,04 0,95 0,00 0,01 The data in Table  21  are plotted in Figure  4. Figure  4  shows that negation marking varies significantly depending on the type of NPI. Preverbal quantifiers are in complementary distribution with N2 and, in strict negative concord languages, always require N1.
Conclusions and open issues The intent of this paper was twofold: methodological and theoretical. On the methodological side, this work aimed at exploring the feasibility of multiple linear regression on raw data collected by dialectological atlases. I showed that, to do statistical analysis, we need to adapt datasets, merge various primary sources, and aggregate factors in order to avoid collinearity and obtain significant statistical results. On the theoretical side, I focused on the distribution of N1 and N2 across central Romance dialects and across syntactic contexts. I proved that models including both geolinguistic and grammatical factors always fit the data better than models in which grammatical variables are not taken into account. In my study, I focused on two kinds of grammatical variables: i) clause typing (in particular, I tried to verify whether veridicality affects the distribution of N1/N2) and ii) negative concord, i.
g. in infinitives), the role of N2 in licensing indefinite objects introduced by the preposition de, etc. Some of the above properties are arguably related to the typology/etymon/shape of N2s, which needs to be encoded in our dataset. To shed light on these issues we need to integrate data from more recent dialectological enterprises such as the ASIt (Syntactic Atlas of Italy), the Thesoc (Thesaurus Occitan), or the wealth of data published in.
Frequency/Probability of N2 w. r. t. Clause in a model with factors Region and Clause (AIS dataset, only 'northwestern' datapoints, excluding tokens containing NPIs and collinear values) Rbrul cannot weight all factors together because Clause and Person are highly collinear, as mentioned in Section 3. 1. I then tested NPIs, which, as previously mentioned are not free to co-occur with N2. The data in Table 10 confirm that not all NPIs are incompatible with N2: negative concord involving N2 is marginally allowed when NPIs are embedded in adjunct PPs or with a negative coordination of the type neither… nor.
These facial variations were highly correlated with the calvarial outline, particularly the degree of frontal and occipital inclines. An allometric pattern was detected in facial proportions in relation to overall cranial size; in larger crania, the facial profiles tend to be longer and narrower, as demonstrated among many American natives and Northeast Asians. Although our study did not include data on environmental variables that are likely to affect cranial morphology, such as climate or dietary conditions, the large datasets of homologous cranial models will be usefully available for seeking various attributions to phenotypic skeletal characteristics. Geographic variations in the human cranial form have been studied for a long time. Numerous researchers have assessed the diversity in regards to environmental adaptation and/or natural selection, particularly in terms of climatic factors  [1] [2] [3] [4] [5] [6] [7]  or mastication functions related to dietary conditions  5, [8] [9] [10] [11] [12] [13].
To interpret the variations in cranial form, we applied PCA and receiver operating characteristic (ROC) analysis to the datasets of our created homologous models. The results will provide a better understanding of global variations in cranial form, including regional patterns and the descending order of variations, correlative variations between cranial segments, and the existence of allometric trends. Although this study does not deal with the data for external variables represented by climate or dietary conditions that likely affect cranial form, the geographical patterns of cranial form recorded in our study will be useful for exploring ecological, biomechanical, and genetic factors of cranial variation.
Results PCA and ROC analysis. Table  2  presents the eigenvalues and contribution rate of PCA applied to the non-normalized datasets of 17,709 vertices (53,127 XYZ coordinates) of the 342 homologous cranial models. Consequently, 14 primary principal components were detected with contribution rates greater than 1% of the total variance, with the cumulative proportion of variances being 83. 68%. The loading vectors of the 14 principal components are recorded in Supplementary Table  S1 , and component scores computed for the 342 cranial samples are provided in Supplementary Table  S2. This study evaluated the first nine components with contribution rates greater than 2%, some of which showed substantial and significant geographical diversities in the cranial form. Figure  2  depicts the curves drawn by ROC analysis to elucidate the most effective component of the PCA to characterize or divide each sample assemblage by major geographic units (e.
In addition to these facial features, the inclination of the occipital bone at the lambda portion was detected as another characteristic of some Northeast Asians, while a narrow cranial base was described for a part of Southeast Asians. The above description of the principal components and descriptions of PC5 and PC8 were skipped because specific regional features were undetected among the nine major geographic units. PC5 is related to the size of the mastoid processes of the temporal bone, and PC8 reflects the asymmetry of the overall cranial shape, both of which show parallel variations among the nine geographic sample assemblages. In addition to the scatter diagram of the PCA scores at the individual level, we provided scatter plots by group averages for gross comparisons. For this purpose, average cranial homologous models were generated based on vertex datasets of individual homologous models of 148 ethnic groups.
Discussion Through analysis of datasets of homologous 3D surface models, the following patterns of variation were demonstrated in the cranial form. The first component in PCA was related to the overall size of the cranium. The small crania of South Asians, including specimens from India, Sri Lanka, and Bangladesh Andaman, have long been known to be associated with their small body size corresponding to Bergmann's Rule or Island Rule 61 based on ecogeographical conditions  3, 5, 16, 25, 27, 62. The former is related to temperature, and the latter depends on the available space in the niche and food resources. In the shape components, the largest variation was detected in the length/breadth ratio of the cranial vault. This characteristic was assigned PC2, which describes the close affinity between the proportionally elongated crania of Australo-Melanesians and Africans and dissimilarities with the globular crania of some Europeans and Northeast Asians.
Methods Cranial specimens and 3D surface scanning. This study included 342 male cranial specimens selected from 148 populations from nine geographic units (Table  1 ). Most of the groups were geographically native specimens, whereas some groups (listed in italics) in Africa, Northeast/Southeast Asia, and America were defined by ethnicity. Many of cranial specimens were selected from cranial measurements database after Martin's craniometric definitions  79  provided by Tsunehiko Hanihara. We chose representative male skulls from each ethnic group worldwide. To identify the individuals representing each population, we calculated the Euclidean distance based on 37 cranial measurements from the population mean to all individuals belonging to the population. In most cases, we selected 1-4 specimens with the smallest distance from the mean (Supplementary Table  S4 ).
For the groups, some specimens were randomly selected if they were absent from the list in Hanihara's measurement database. For statistical comparisons, 148 population samples were grouped according to the major geographical units, as shown in Table  1. The group "Africa" consisted only of samples from the sub-Saharan regions. The samples from North Africa were included in the "Middle East, " together with the environmentally similar West Asians. The "Northeast Asia" group included only people of non-European origin, and the "America" group consisted only of Native Americans. In particular, this group is distributed over a broad area of the North and South American continents in different environments. Regardless, we dealt with American samples in this single geographical unit, considering the population history of Native Americans who are assumed to be of Northeast Asian origin, regardless of multiple migrations  80.
We recorded the 3D surface data of these comparative cranial specimens by using a high-resolution 3D scanner (EinScan Pro produced by Shining 3D Co Ltd, minimum resolution: 0. 5 mm, https:// www. shini ng3d. com/) and then generated mesh wire models consisting of approximately 200,000-400,000 vertices, using the included software to fill holes and smooth edges. Generation of the homologous model. Template with landmark setting. In the first step, we created a single-template mesh model of the cranium consisting of 4485 vertices (8728 polygon faces) using scan data from an arbitrary cranium. From the template mesh model, the area of the cranial base consisting of the sphenoid bone, petrous part of the temporal bone, palate, maxillary alveolar, and teeth were removed. The reason was that these structures are sometimes incomplete or difficult to complete due to thin or fine sharp segments such as pterygoid planes and styloid process, tooth abrasion, and/or inconsistency of tooth sets.
The cranial base around the foramen magnum, including the basion point, was not removed because it is an anatomically important position with which the cervical vertebrae are articulated, and it is necessary to assess cranial height. The template model was formed bilaterally symmetrically by using a mirror ring. Isotropic remeshing was performed to transform the polygon shape to be as equilateral as possible. Next, 56 landmarks were assigned using the HBM-Rugle software to the anatomically corresponding vertices of the template model. Landmark setting generates accuracy and stability for landmark localization and ensures homology in these locations in the created homologous models. They are identifiable according to their specific characteristics, as shown in Supplementary Table  S5  and Supplementary Fig.
The X axis is the line connecting the right and left porions, and X+ is the right porion. The Y-Z plane passes the midpoint of the right and left porions and the nasion; Y+ is upward and Z+ is forward. The reference point (origin: zero coordinate) was set at the intersection of the Y-Z plane (median plane), X-Z plane (Frankfort plane), and X-Y plane (coronal plane). Template fitting. We used HBM-Rugle software (Medic Engineering, Kyoto, http:// www. rugle. co. jp/) to create homologous mesh models by template fitting using 56 landmarks (left side of Fig. 1 ). The main components of this software were developed originally at the Digital Human Research Center, National Institute of Advanced Industrial Science and Technology, as HBM, which has functions for template fitting using landmarks and creating fine-mesh models using subdivided surfaces  82.
In the later version of this software (mHBM)  83  , a function for template fitting without using landmarks was added, and the fitting performance was improved. HBM-Rugle incorporates mHBM software and has additional functions for user convenience, including setting coordinate systems and size adjustment of input data. The robustness of the fitting accuracy of this software has been verified in several studies  52, [54] [55] [56] [57] [58] [59] [60]. In HBM-Rugle template fitting using landmarks, the template mesh model is imposed onto the target scan data by rigid registration based on the ICP technique (minimizing the sum of distances between the corresponding landmarks of the template and target scan data); the template is then fitted onto the target scan data by nonrigid mesh deformation.
The mean distance between the corresponding landmarks of the homologous model and the target scan data was < 0. 01 mm in all samples. The mean distance between the data points of the homologous model and the target scan data was 0. 322 mm, as calculated using a function of HBM-Rugle (Supplementary Table  S2 ). PCA and ROC analysis. To interpret the variations in cranial form, the 17,709 vertices (53127 XYZ coordinates) of all homologous models were analyzed by principal component analysis (PCA) using the software HBS produced by Digital Human Research Center at the National Institute of Advanced Industrial Science and Technology, Japan (distributer: Medic Engineering, Kyoto, http:// www. rugle. co. jp/). Then, we attempted to apply PCA to the non-normalized dataset and the dataset normalized by centroid size.
The degree of capability of distinguishing is assessable by the area under the curve (AUC), of which the component of PCA with greater value is at better distinguishing between groups. Then, the chi-square test was performed to evaluate the significance level. ROC analysis was performed in Microsoft Excel using the Bell Curve for Excel (version 3. 21) software. To visualize geographic differences in cranial form, scatter diagrams were created using the PC scores that most effectively differentiated groups from major geographical units. To interpret the principal components, the vertices of the model that were highly correlated with a principal component were visualized using a color map. In addition, virtual representations at both ends of a principal component axis located at ± 3 standard deviations (SD) of the principal component scores were calculated and are provided in Supplementary Videos.
Allometry. Allometry was used to define the relationship between the cranial shape and size factors estimated in the PCA analysis. The analysis was applied to the primary components with contribution rates > 1%. One limitation of this PCA is that the form components might be not be indicative of shape alone because the non-normalized data sets did not have all size factors removed. In addition to using non-normalized data sets, we analyzed allometric trends using PC score sets based on data normalized by centroid size, applied to the primary components with contribution rates > 1%. The allometric trend  85  was tested using the equation Y = aX b , where Y is the score of the form or shape component, X is the centroid size (Supplementary Table  S2 ), a is a constant value, and b is the allometric coefficient.
Acknowledgements The authors are grateful to two anonymous reviewers and the Editor for their useful suggestions, comments, and manuscript corrections, which significantly improved the quality of this article. We thank the  Duckworth Laboratory, University of Cambridge, Smithsonian Institution, Musée de l'Homme, Paris, Natural History Museum, London, Peter the Great Museum of Anthropology and Ethnography. We are grateful to  Marta Mirazon Larh  and  Trish Biers ,  Department of Biological Anthropology, University of Cambridge, David Hunt, Department of Anthropology, Smithsonian Institution, Véronique Laborde, Département Homme et Environnement, Musée de l'Homme, Paris , and  Rachel Ives ,  Department of Earth Sciences, Natural History Museum, London , for their helpful support in accessing the human skeletal specimens.
Acknowledgements We acknowledge the  European Research Council  for funding a  Proof of Concept Grant  to RPE ( LipDatH2020 ERC-2018-PoC/812917 ) financing a postdoctoral contract to EC. NERC  are thanked for partial funding of the  National Environmental Isotope Facility  (NEIF;  NE/V003917/1 ). The authors wish to thank the  NERC  ( NE/V003917/1 ), the  European Research Council  ( FP7-IDEAS-ERC/340923 ) and the  University of Bristol  for funding GC-MS and GC-C-IRMS capabilities used for this work. We thank the  BRAMS  facility established with funding by  NERC ,  BBSRC  and the  University of Bristol. The Royal Society is thanked for funding a fellowship to MRS ( DHF\R1\180064  and  RGF\EA\181067 ).
Introduction The Southern Caucasus is a highly volcanic region, very rich in obsidian deposits. These have been extensively exploited by the local population since most ancient times: indeed, with frequencies which not rarely exceed 90% of the total, obsidian represents by far the most common raw material of the local chipped lithic assemblages. Over the last 30 years, the database of analysed artefacts from archaeological sites in the region dramatically increased, and the characterisation of the different local sources has greatly improved  (Chataigner and Gratuze, 2014a; Chataigner et al. , 2014; Biagi and Gratuze, 2016; Biagi et al. , 2017; Frahm et al. , 2017; Cherry et al. , 2010). It has thus not only been possible to establish that obsidian from South Caucasian sources circulated (albeit in limited amounts) beyond the region, in particular toward Northern Mesopotamia and Northwestern Iran (e.
The Neolithic site of Khramis Didi Gora in Kvemo Kartli and the Chalcolithic site of Tsiteli Gorebi in Kakheti followed the latter model, whereas the Early Iron Age (EIA) site of Naomari Gora in the same province exhibited almost 90% of obsidian from the Chikiani source. Interestingly, the Chikiani source was not recorded at Tsiteli Gorebi. This could suggest that the Alazani valley, where Tsiteli Gorebi is located, belonged to a separate procurement network; however at the Late Chalcolithic site of Padar in Azerbaijan, located further east in the same river basin, Chikiani obsidian was prevalent (more than 60%), although in the framework of a multisource scenario. Clearly, data from a larger number of sites of different periods are needed for a well-founded evaluation of the local and diachronic variability between these two contrasting models of obsidian procurement patterns in Eastern Georgia.