dataesr/scientific-paragraphs-categorization huggingface dataset:
Past bird communities are still under-studied in several Caribbean regions, including the Lesser Antilles. In order to improve our understanding of this area's avifauna, we explore morphometrical variations of the carpometacarpus (CMC) within West Indies Mimidae species. We combine geometric morphometric (GMM) and conventional osteology focusing on characters of the entire or distal portion of the CMC. Morphological variation related to their phylogenetic history is investigated using uni-and multi-variate statistics, and the expression of certain osteological characters. Fossil bone remains from the Guadeloupe Islands were included in the datasets to test the applicability of these results to the archaeological and paleontological record. Our results are consistent with the known phylogeny of Mimidae.
MODERN SPECIMENS The modern Mimidae sample comprises 55 specimens from 7 of the 10 genera of this family and 9 of 34 species known worldwide. The material comes from eight institutions and different geographic areas (see  Ten members of the Mimidae family currently occur in the Caribbean, either as breeding or migrating populations  (Raffaele et al. 2010 ), all of which were included in the analyzed sample, apart from the Grey Trembler Cinclocerthia gutturalis. As Caribbean vertebrate diversity is poorly represented in museum collections  (Lefebvre & Sharpe 2018) , the dataset required loans and the 3D modeling of specimens from various institutions (Table  1 ). In addition, the osteology of several rare and endangered species, such as the White-throated Thrasher Ramphocinclus brachyurus  (Mortensen et al.
MICRO-CT AND 3D IMAGING All but one of the CMC were scanned with General Electric Vtome x|s x-ray microtomograph of the PACEA laboratory (housed at the UMS Placamat, University of Bordeaux, France) using a source voltage of 100 kV, 120 µA source current, 500 ms exposure time, 2550 projections, and 0. 1 mm copper filter. Voxel sizes of the acquisitions ranged between 10 and 17 microns, depending on the specimen. For conservation reasons, one of the White-Breasted Thrasher CMC (UMMZ-158596) was scanned at the host institution. Surface models were generated with the Avizo Isosurface module (version 9. 3. 0) using all voxels with a greyscale value above a selected threshold  (Wils 2016). This type of surface model provides more detail than volume rendering techniques  (Weber & Bookstein 2011)  and generates triangulated surfaces that can be easily manipulated and measured.
Inter-genus shape variation The separation between the pan-continental and Antillean endemics clades observed in the complete dataset is less evident within the PC1-2 and PC2-3 morphospaces (Figure  7A  and 7B; Supplementary file 5). Several genera are, however, set apart along the second axis, including Dumetella and Mimus. Cinclocerthia and Ramphocinclus stand out from the other Antillean endemic genus along axis 2. Allenia and Margarops cluster together in the center of the morphospace. Cinclocerthia and Dumetella, which were not clearly differentiated by the complete dataset, now appear separated along PC2. A classification matrix correctly predicted more than 50% for each gender of Mimidae, with a total correct attribution of 71% across all genera (Table  7 ), slightly less than when the complete dataset is considered.
APPLICATION TO THE FOSSIL RECORD Osteological characters and metric Our data indicate that identifying fossil Mimidae specimens to species based uniquely on osteological characters is unreliable (Table  8 ). Three fossils exhibit combined A2-E2 states, a character absent in the modern sample, and at least three species attributions are possible for several other specimens. More certain species attributions are, however, possible for some fossils when osteological characters are combined with a consideration of size differences. The AC3-2019-O-1438 and PGR6-2016-O-182 Table  7  Confusion matrix for genera and species based on a linear discriminant analysis performed on the first five axes in the distal dataset.
Species attributions based on the complete dataset The centroid size (Figure  8  The four complete archaeological specimens were plotted against the PCA of the reference dataset (Figure  9 ). PGR6-2011-O-51 falls outside the range of variation of the different species sampled in our study except along PC1-2, where it fits within the morphospace of Allenia (Figure  9A ). GC2-2014-O-1 plots with the pancontinental group, close to the convex hulls of Mimus in the PC1-2 and PC2-3 morphospaces. The situation is less clear for GC2-2014-O-2, as it falls between the pan-continental and Antillean clades (Figure  9A.
Species attribution based on the distal dataset The centroid size for all PGR6 specimens falls between the genus Cinclocerthia and Ramphocinclus and the smaller M. gilvus (Figure  11 ). The smallest of the PGR6 specimens is also close to Dumetella. Two size groups can be distinguished amongst the Grotte Blanchard specimens: one with small centroid size consistent with Mimus species and larger individuals of Cinclocerthia and Ramphocinclus, the other with larger centroid size compatible with Allenia. The centroid size of GC2-2014-O-2 falls between those of Allenia and Margarops, and GC2-2014-O-1 presents a centroid size similar in size to that of Ramphocinclus, Cinclocerthia, and M. gilvus. The fossils were projected on the PCA run for the distal dataset (Figure  12 ). When the first five PC scores are plotted, the distinction between several genera is not evident, while others, such as Cinclocerthia and Ramphocinclus, are clearly distinct from other modern specimens within the PC1-2, PC2-3, PC3-4, and PC2-4 morphospaces.
A MANOVA reveals fewer significant differences than for the complete dataset, with the exception of Allenia and Mimus (Table  9 ). Fossils from PGR6 tend to group together near the ranges of Cinclocerthia and Ramphocinclus in all morphospaces. Specimens from Grotte Blanchard also group together close to the ranges of Margarops, Mimus and Allenia. The Grotte des Bambous fossil does not plot within a particular morphospace of the modern specimens but falls within or relatively close to the range of Margarops, depending on the PC axis considered. AC3-2019-O-1435 fits within the morphospace of Allenia in all PC morphospaces except PC3-4. GC2-2014-O-1 plots close to within the range of Cinclocerthia (cf. PC2-3 and PC3-4), while GC2-2014-O-2 falls within the range of Allenia for all PC. A phenogram performed on the first five axes of the distal dataset (Figure  13.
SYNTHESIS Species attributions based on traditional osteological characters (Table  8 ) and geometric morphometrics (Table  10 ) for complete CMC (Figure  14 ) and the distal portion of this bone (Figure  15     Two different size groups are evident in the Grotte Blanchard and Grotte Cadet 2 fossils. While the large specimen GC2-2014-O-2 cannot be attributed to a particular species based on the complete dataset, its size (conventional and CS) and osteology are compatible with A. fusca, an attribution equally supported by both approaches to the distal dataset. The large fossils GB-2008-O-349 and GB-2014-O-92 are also impossible to unambiguously attribute to a particular species due to diverging results for the different methods. For the first specimen, its osteology and position close to the convex hull of M.
fuscatus in morphospaces 3-4 and 4-2 are consistent with M. fuscatus and, while the second presents the same traits, its morphometry is less certain due to slight damage affecting parts of the bones. However, the two fossils are smaller than M. fuscatus but similar in size to A. fusca, making it compatible with both species. In terms of the smaller fossils, both datasets attribute GC2-2014-O-1 to C. ruficauda based on both its size and geometric morphometric criteria. Although this specimen displays the combination of states A2-E2 which is absent in the modern sample, the highly pronounced proximal concavity of the cranial edge is typical of modern specimens of C. ruficauda, which argues in favor of referring the fossil specimen to this species. The position of BAM-2014-O-20 falls within several convex hulls, however its size and morphometry are compatible with C.
ruficauda or M. gilvus. Like the previous individual, this specimen presents both the combination of states A2 and E2 and a highly pronounced proximal concavity, suggesting C. ruficauda for attribution. Both GB-2008-O-361 and GB-2014-O-156 are referred to C. ruficauda based on the distal and complete dataset, respectively. The distal dataset does not allow GB-2014-O-156 to be attributed to a species, although the specimen's osteological characters and size are compatible with C. ruficauda. GB-2014-O-35, while morphologically similar to M. fuscatus (based on the GMM), its small size is incompatible with this species. Moreover, the specimen plots close to C. ruficauda in morphospaces 1-2 and 2-3, a potential attribution equally supported by its size and osteology. Finally, this fossil specimen is similar to the previous two, suggesting it belongs to the same taxon.
Natural barriers (e. g. middle mountains, distance between islands) and the multiple habitats of the Caribbean islands would favor the isolation of species, leading to the appearance of morphological or behavioral differences, as previously discussed for Amazonian passerines  (Hayes & Sewlal 2004). Phylogeny nevertheless appears to play a primary role in explaining CMC shape. Our results reveal high evolvability of this anatomical element between Mimidae species. Similarities between the morphological and phylogenetic signature equally raises questions concerning the impact of population history. The two previously identified Antillean and pan-continental Mimidae clades based on genetic analyses  (Lovette and Rubenstein 2007; Lovette et al. 2012; DaCosta et al. 2019 ) are equally evident in the morphometric analysis of the complete dataset.
This species is also absent from the coastal areas of the wet islands, which are covered by dry or seasonal evergreen forest, at least during the dry season  (Steadman et al. 1997; Zusi 1969 ). As such, the Brown Trembler is the only Mimidae species to show a preference for tropical rain forest  (Cody 2005). The fossil specimens identified in this study, in contrast, depict a much wider range of past habitats occupied by this bird, including dry islands. Its presence in natural Pleistocene fossil-bearing deposits on Marie-Galante (Grotte Blanchard) excludes these specimens having been imported by humans. In addition, this is consistent with the report of Brown Trembler remains from Burma Quarry on the neighbouring island of Antigua  (Pregill et al. 1988). Moreover, our comparative dataset includes all others dry forest-dwelling Caribbean Mimidae, ruling out the determination of the Brown Trembler as a misidentification of a distant species  (Stewart 2005) , above all the White-breasted Thrasher, which occurs in the dry coastal forests of St.
Confusion matrix based on a linear discriminant analysis performed on the first five axes in the complete dataset. should be interpreted with caution. The same tendency can be observed as for the complete dataset: Margarops fuscatus is the largest species, followed by Allenia fusca, and Dumetella is the smallest. The same size ordering is observed in the whole dataset, apart from M. gundlachii (which is no longer distinct from T. rufum and M. polyglottos), and from C. ruficauda (which overlaps with the intermediate Mimus species M. polyglottos). Although the size gradient observed in the complete Mimus dataset is less clear, M. gilvus is still the smallest species while M. polyglottos and M. gundlachii overlap considerably.
ACKNOWLEDGEMENTS This study was conducted in the MoMi project funded by the LaScArBx ANR-10-LABX-52. It also benefited from funding and material collected in the ECSIT Project: « Écosystèmes insulaires tropicaux, réponse de la faune indigène terrestre de Guadeloupe à 6 000 ans d'anthropisation du milieu », with financial support from the European PO-FEDER program (grant n° 2016-FED-503), the Guadeloupe Regional Council, and the DAC of Guadeloupe. The µCT equipment was acquired thanks to funding from Nouvelle-Aquitaine Regional Council & the Cluster of Excellence in Archaeological Sciences in Bordeaux (ANR-10-LABX-52). The authors are grateful to the institutions and curators who facilitated the loans of comparative specimens: S. Guimaraes (MEC), J. Fuchs & C. Lefèvre (MNHN), M. Peck (ROM), J.
A writing system is a graphic code, i. e. , a system of standardized pairings between symbols and meanings in which symbols take the form of images that can endure. The visual character of writing implies that written characters have to fit constraints of the human visual system. One aspect of this optimization lays in the graphic complexity of the characters used by scripts. Scripts are sets of graphic characters used for the written form of one language or more. Using computational methods over a large and diverse dataset (over 47,000 characters, from over 133 scripts), we answer three central questions about the visual complexity of written characters and the evolution of writing: (1) What determines character complexity? (2) Can we find traces of evolutionary change in character complexity? (3) Is complexity distributed in a way that makes character recognition easier? Our study suggests that (1) character complexity depends primarily on which linguistic unit the characters encode, and that (2) there is little evidence of evolutionary change in character complexity.
Introduction Writing is a graphic code, i. e. , a system of standardized pairings between symbols and meanings in which symbols take the form of images that can endure  (Morin, Kelly, & Winters, 2020). It is a visual communication system which takes "the form of visible marks on the surface of a relatively permanent object"  (Treiman & Kessler, 2011)  and encodes a natural language  (Morin et al. , 2020). Writing systems are based on characters organized into sets, here called scripts. Characters, in our definition, are the basic symbols used to visualize (write or print) a language. A script as we define it is a set of graphic characters used for the written form of one or more languages. A script is also sometimes one of several that are used for a language's writing system. Scripts, writing systems, and spoken languages rarely overlap perfectly.
For instance, the Latin script is used by a variety of writing systems, to write a diversity of languages. Scripts do not determine what writing encodes, but they determine what writing looks like. For instance, the character a in the Latin script is defined by a basic shape which can be used in different languages to encode different sounds, depending on the context (language, word) in which it is used. Conversely, a writing system can be written using different scripts, e. g. , Serbo-Croatian. Written languages contrast with spoken languages. Writing is a relatively recent innovation: it can be traced back to a few invention events that occurred no more than a few thousand years ago. Writing requires, to a much larger degree than speaking, an explicit and deliberate effort in learning and transmitting it.
While spoken languages extensively recruit auditory perception, written languages rely on the visual modality. This implies that written characters have to fit constraints of the human visual system  (Dehaene, 2010; Dehaene & Cohen, 2007). At least two characteristics of writing systems reveal their adaptation to the human visual system. The characters of scripts are anisotropic with respect to the orientation of strokes within letters (preference for vertical and horizontal strokes over obliques), and of mirror symmetries (vertical symmetry being preferred to horizontal symmetry), two properties that can be predicted on neuroscientific grounds  (Morin, 2018). They also tend to mimic natural scene statistics, by extensively using basic topological shapes that recur in the natural visual environment  (Changizi, Zhang, Ye, & Shimojo, 2006; Testolin, Stoianov, & Zorzi, 2017) , and cardinal orientations, which are overrepresented in the natural world  (Morin, 2018).
Lexicon Script: A set of graphic characters used for the written form of one or more languages, or the "graphic form of the units of a writing system"  (Coulmas, 2002, p. 35). A script can be used by one or more writing system(s). For instance, the Latin script is used for several writing systems, including the English alphabet, or the Polish alphabet. Writing system: A set of conventions linking a script to the sounds and words of a language. A writing system is usually based on one script only. (Writing system) type: A way to categorize scripts based on the semiotics of the writing system they are used for, i. e. , the linguistic unit their characters encode. Well known types include alphabets, syllabaries, logographies, etc. Here we often write "type" as short-hand for "writing system type".
What determines the graphic complexity of a script's characters: Type, size, phylogeny? Two main drivers of character complexity have been hypothesized: its graph inventory size, and the type of the writing system it is associated to  (Chang et al. , 2016; Changizi & Shimojo, 2005). The size of a script's inventory is the number of characters included in the script. For instance, the graph inventory size of the Latin [Latn] 1 script is 52, as it includes 52 unique characters (we consider upper-and lower-case letters to be distinct characters). Writing system type is a way to categorize scripts based on the semiotics of the writing system they are used for, i. e. , the linguistic unit their characters encode. Well known types include alphabets, syllabaries, logographies, etc. In addition to graph inventory size and writing system type, we add phylogenetic influence: the character complexity of one script can also be influenced by the script from which it descends.
Size hypothesis: Scripts with larger graph inventories have more complex symbols Studies investigating the relation between complexity and graph inventory size have yielded conflicting results. Changizi and Shimojo (2005)  found that character complexity was of three strokes on average, independently of the script considered, while  Chang et al. (2016)  and  Chang, Chen, and Perfetti (2018)  found that character complexity increased with the number of characters included in a script, and was influenced by writing system type. Writing system type is, in this case, thought to influence the complexity of characters through the number of characters required by the mapping between characters and linguistic units. According to  Chang et al. (2016) , "the need for complexity is driven by the size of the grapheme inventory, which in turn is driven by the size of linguistic units to which they map: phonemes, syllables, syllabic morphemes, in increasing order" (p.
Homogeneity hypothesis: Most variance in character complexity is captured at the level of the script Several arguments suggest that most of the variance in character complexity should be captured by knowing which script a character is from. First, inclusion in a given script captures many important sources of variance in character complexity that do not vary at the level of individual characters. This includes the material that the script is usually written on; the shape of the basic strokes making up the script; or general stylistic influences. Second, something like the principle of uniform information density  (Jaeger, 2010) , which guides spoken language production, may also constrain written language. If true, this would mean that writers maintain a more or less constant level of complexity throughout the various letters that they write.
, 2006; Zhang, Zhang, Xue, Liu, & Yu, 2007). This effect of complexity is robust to participants' familiarity or experience with the images  (Byrne, 1993) , and to levels of noise, overall contrast, or eccentricity in the visual field  (Shu, Chen, Anderson, Wu, & Xuan, 2003). Graphic complexity also weighs on the working memory load, making visual search harder  (Alvarez & Cavanagh, 2004). Finally, the effects of graphic complexity occur early in the visual processing of wordsearlier than orthographic or semantic effects  (Dufau, Grainger, Midgley, & Holcomb, 2015). These results imply that a script can become more legible (up to a point) by decreasing its characters' complexity. Complex drawings and scribbles are known to simplify in experimental settings, because simpler shapes take less effort both to remember and to produce.
, 2019)  where useful information is located first in reading and writing order. We expected to find that letters in scripts written and read from left to right (like Latin) are "left-heavy": their left half would be more complex than their right half. Conversely, letters in scripts written from right to left should be found to be "right-heavy". When testing this prediction, we must control for a possible tendency for visual information to concentrate on the left or right side of characters, independently of reading direction. Visual perception has inbuilt asymmetries between the left and right visual hemifields, at least for some domains such as face perception (e. g. Voyer et al. , 2012). Whether such an asymmetry applies to the perception of written characters independently of reading direction is, however, far from clear.
Some studies suggest that information presented in the right-hemifield is given more weight  (Hardyck, Tzeng, & Wang, 1977) , while others find the opposite effect  ( Śmigasiewicz et al. , 2010 for Latin characters, Chung et al. , 2017 or Zhou, Gao, Chang, & Su, 2019  for Chinese characters). We nevertheless controlled for laterality, as a precaution. We pre-registered two predictions based on the hypothesis that visual information clusters on the side of characters that the reading eye encounters first. Visual complexity should be higher in character halves that come first in reading order: left for Left-right scripts (i. e. scripts customarily written and read from left to right), right for Right-left scripts. Our second prediction was that complexity differentials between halves are specifically predicted by order: left halves are not more complex than right halves when they come last in reading order, and vice-versa.
Inventory constitution 2. 2. 1. Script-level inclusion rules The inventory of scripts included in our study was compiled from the Unicode 10. 0, updated according to the Unicode 11. 0 (The Unicode Standard, Version 11. 0, (Mountain View, CA: The Unicode Consortium, The Unicode  Standard, Version 11. 0, 2018. ISBN 978-1-936,213-19-1) , and enriched with official proposals to encode new scripts not included in the standard, but under consideration at data collection time (November-December 2018). This study excluded the following scripts. Secondary scripts, defined by  Morin (2018)  as scripts used by a writing system that encodes another system, e. g. Stenographics such as Duployan shorthand  [Dupl] ); non-visual scripts (e. g. Braille [Brai], a haptic script); scripts that do not directly encode a spoken language (e.
Description of the dataset Our dataset was large and diverse: it included 47,880 characters from 133 scripts, comprising (see Fig. 1 ) 5 East Asian scripts, 23 Phoenician (European) scripts, 35 Indian scripts, 24 Middle East scripts, 23 Modern Inventions, 11 South East Asian Insular scripts, and 12 Mainland South East scripts. As for type of writing system, 17 abjads, 56 abugidas, 44 alphabets, 1 featural system, 2  4 logosyllabaries (morphosyllabaries), and 11 syllabaries were included.