dataesr/scientific-paragraphs-categorization huggingface dataset:
These radiocarbon ages and those initially published for Ali Tappeh by  McBurney (1968)  have been calibrated at 95. 4% (2 sigma) probability using the Oxcal program (v4. 3) of C. Bronk Ramsey and the INTCAL13 dataset  (Reimer et al. , 2013). The AMS radiocarbon ages of materials from Hotu, Ali Tappeh and Komishan Caves from the 1990s and 2010s and the recently-discovered Komishani Tappeh have been calibrated at 95. 4% probability using the Oxcal program (v4. 3) of C. Bronk Ramsey and the INTCAL13 dataset  (Reimer et al. , 2013)  and the Calib program (v7. 10) of Reimer  (Stuiver et al. , 2018). These dates, after eliminating the two dates on marine shells, suggested to be influenced by natural hydrocarbons, and one date, GX-690, deemed unreliable by its author (see  Hedges et al. , 1994) , were organised according to increasing BP ages.
Acknowledgements The palynological research benefited from a small research grant from the  British Academy  ( SG150522 ). A. Rozeik  ( Brunel University London, UK ) and  J. -C. Mazur  ( CEREGE, France ) are acknowledged for extracting the palynomorphs in the laboratory. We are thankful to  F. Marret  ( University of Liverpool, UK ) who confirmed the identification of the Galeacysta etrusca-Spiniferites cruciformis form. Funding for recent excavations and radiocarbon assays from  Komishani Tappeh  was generously provided by the  Iranian Centre for Archaeological Research in Tehran (Iran)  and the  National Geographic Society  (Project  9870-16 ) in Washington, D. C. ,  USA. We are grateful to  A. Barlow  and  C. Pickard  ( University of Edinburgh, UK ) for support and advice on the faunal stable isotope analysis and  J.
("Roland TB-303 Acid House Flashback") In other words, these six knobs, when turned to their maximum level, combined with the oscillator in square wave position, create a distorted, saturated, squelchy bass sound. This, added to the very artificial drums sounds produced by the Roland TR-909, gives a grainy touch to the track that we can also find in punk rock music. All these samples, which follow the classic 4/4 techno pattern, may have been mixed around 145 BPM (beats per minutes) by the two DJs which is typical of acid techno that is generally mixed between 145 BPM and 155 BPM and thus faster than techno music or acid house which are mixed between 118 and 135 BPM. When I say "may," it is because most DJ software programs like Serato or Virtual DJ naturally read the track at 145. 41 BPM.
Multiphoton microscopy Two-photon excitation microscopy was also used for fluorescence analysis. Paper sheets were positioned on a stage of a customized Olympus multiphoton microscope BX61WI/FV1200MPE with a 25X immersion objective (1. 05NA, 2. 0 mm working distance) coupled with a tunable femtosecond Ti:Sapphire pulsed laser (Chameleon Ultra II, Coherent) for the excitation  (Bardet et al. 2016). Image stacks were acquired under 810 nm excitation for second harmonic generation (cellulose) and fluorescence (RBITC for fines, autofluorescence for lignin) wavelength with FluoView FV1200 software (v4. 1. 1. 5, Olympus). Each acquisition in photon-counting mode produces a 3D stack of 640 9 640 9 53 pi, with a sampling step of 2 lm and a dwell time of 20 ls pi -1 (input laser 20 mW). The different components of the emitted light from the sample were separated using a dichroic mirror (450 nm) and detected by a pair of photomultiplier tubes preceded by fluorophore specific emission filters (607/36 for fluorescence in red, 405/10 for second harmonic generation in green).
Acknowledgments Open access funding provided by  Graz University of Technology. The authors acknowledge the industrial partners  Sappi Gratkorn ,  Zellstoff Po  ¨ls and  Mondi Frantschach , the  Austrian Research Promotion Agency (FFG)  (Grant No. 861476 ), COMET,  BMVIT ,  BMWFJ , the  Province of Styria and Carinthia  for their financial support of the K-project  Flippr 2 -Process Integration. This work was supported by grants from the  LabEX SigmaLim  ( ANR-10-LABX-0074-01 ). We also thank  Inge Mu ¨hlbacher , who proved insight and expertise that greatly assisted this research. Compliance with ethical standards.
I am aware, even as I am writing this, that polar bears are indeed starving to death in the Canadian North, a consequence of global warming that could hardly have been anticipated a decade ago, although efforts to document the disintegration of Arctic and Antarctic ice shelves date back to the mid 1990s. See National Snow and Ice Data Center. State of the Cryosphere. https://nsidc. org/cryosphere/sotc/iceshelves. html.
Identifying the presence of animals based on faecal deposits in modern and ancient environments is of primary importance to archaeologists, ecologists, forensic scientists, and watershed managers, but it has proven difficult to distinguish faecal material to the species level. Until now, four 5β-stanols have been deployed as faecal biomarkers to distinguish between omnivores and herbivores, but they cannot distinguish between species. Here we present a database of faecal signatures from ten omnivore and herbivore species based on eleven 5β-stanol compounds, which enables us to distinguish for the first time the faecal signatures of a wide range of animals. We validated this fingerprinting method by testing it on modern and ancient soil samples containing known faecal inputs and successfully distinguished the signatures of different omnivores and herbivores.
For faecal fingerprinting with stanols, it is therefore important to restrict the analysis to 5β-stanols alone. We hypothesized that including the analysis of other 5β-stanol compounds, even though they are present in lower quantities in animal faeces, would significantly improve our ability to derive species-specific faeces fingerprints using multivariate statistics. To test this hypothesis, we analysed the concentrations of eleven faecal stanols in 90 reference samples of animal faeces collected from ten domestic and wild mammalian species (humans, pigs, dogs, cows, horses, goats, sheep, reindeer, lemmings, moose; S1 and S2 Figs, S1 and S2 Tables). We then used multi-variate statistics to develop a faecal fingerprint 'reference library'. Then, we validated this faecal reference database by testing it on modern and ancient soil contexts with known faecal inputs, where wild and domestic animals and humans intermingled.
The capillary column used was 60 m-long with an inner diameter of 0. 25 mm (SLB-5ms, Supelco). Analyses were carried out in selective ion monitoring (SIM) mode (main fragments can be found in S1 Table ). Identification of compounds was made by retention time and mass spectra comparison with those of available standards or data in the literature (S1 and S2 Figs, S1 Table ). Quantification was achieved with 5-point internal calibration curves of available standards with relevant fragments, and comparison with a constant IS concentration added prior to analysis. The limit of quantification was 30 ppb. The SPE-SIM-QP2010 method was performed on waste water treatment plant effluents (for the human reference samples) as previously described  [24]. Briefly, lipids from filtered samples were extracted by solid phase extraction (SPE) through ENVY disks (Supelco) and analysed and quantified by GC-MS according to the ASE-SIM-QP2010 method.
Statistical analysis To compare data generated from different methods and to compare faeces to soil samples, 5βstanol concentrations were transformed into their relative abundance (%) compared to their sum (Fig  1 ). Prior to statistical analyses, 5β-stanol relative abundances were arcsine ( p %)-transformed to normalize distributions and increase homoscedasticity as previously recommended  [53]. Multivariate analyses were performed with open-source R  [54]  and RStudio Desktop  [55]  software, using the FactoMineR package and the related Rcmdr graphical interface  [56]. To investigate the differences between 5β-stanol fingerprints in herbivore faecal samples, principal component analysis (PCA) was performed on the transformed relative abundance of each compound (or variable, S4 Table ) followed by a hierarchical clustering on principal components (HCPC, Fig 2A  -2C ) using Euclidian distances and Ward's method.
38 < R1 < 0. 73 = human,  [20] ). R2 refers to the ratio of distributions of (coprostanol + epicoprostanol)/(24-ethylcoprostanol + 24-ethylepicoprostanol) used to identify the omnivore fingerprint (R2 > 1,  [7] ). R3 refers to the ratio of distributions of epicoprostanol/(cholestanol + coprostanol) used to discriminate the herbivore fingerprint from human (human = 0. 01 < R3 < 0. 1 = cattle and horse,  [21] ). R4 refers to the ratio of distributions of (24-ethylepicoprostanol/24-ethylcoprostanol) + (epicoprostanol/coprostanol) used to discriminate the horse fingerprint from other herbivores (No horse = 0. 8 < R4 < 1. 2 = horse,  [10] ). PCA-4 refers to the predictive PCA and its corresponding HCPC built with the distribution of the four main 5β-stanols (coprostanol, epicoprostanol, 24-ethylcoprostanol and 24-ethylepicoprostanol) in the human, dog, horse and reindeer reference samples from our database.
PCA-11 refers to the predictive PCA and its corresponding HCPC built with the distribution of eleven 5β-stanols in the human, dog, horse and reindeer reference samples from our database. Reindeer and horses are herbivores so diagnostics between ratios and multivariate analyses can be compared. https://doi. org/10. 1371/journal. pone. 0211119. g005 cholesterol and its transformation products 5α-cholestanol, coprostanol, epicoprostanol etc. ), while herbivore faeces were dominated by C 29 compounds (i. e. sitosterol and its transformation products 5α-sitostanol, 24-ethylcoprostanol, 24-ethylepicoprostanol etc. ). In a similar study, Shah et al. [4]  were able to distinguish a human faecal fingerprint from omnivores (dogs. R1 refers to the ratio of distributions of coprostanol/(coprostanol + 24-ethylcoprostanol) used to discriminate the herbivore fingerprint from human (herbivore = 0.
38 < R1 < 0. 73 = human,  [20] ). R2 refers to the ratio of distributions of (coprostanol + epicoprostanol)/ (24-ethylcoprostanol + 24-ethylepicoprostanol) used to identify the omnivore fingerprint (R2 > 1,  [7] ). R3 refers to the ratio of distributions of epicoprostanol/(cholestanol + coprostanol) used to discriminate the herbivore fingerprint from human (human = 0. 01 < R3 < 0. 1 = cattle and horse,  [21] ). R4, the diagnostic ratio for the horse fingerprint, is "not applicable" (NA) because no horses were present on this high Arctic site (see Text). PCA-4 refers to the predictive PCA and its corresponding HCPC built with the distribution of the four main 5β-stanols (coprostanol, epicoprostanol, 24-ethylcoprostanol and 24-ethylepicoprostanol) in the human, dog, lemming and reindeer reference samples from our database.
PCA-11 refers to the predictive PCA and its corresponding HCPC built with the distribution of eleven 5β-stanols in the human, dog, lemming and reindeer reference samples from our database. Reindeer and horses are herbivores so diagnostics between ratios and multivariate analyses can be compared. https://doi. org/10. 1371/journal. pone. 0211119. g006 and dingos), herbivores (pig, lamb, cow, donkey, horse, water buffalo, rabbit, kangaroo, koala) and birds (chicken, duck and turkey) using multivariate analyses (hierarchical clustering and canonical analysis) based on the concentration of steroidal compounds in animal faeces (cholesterol, campesterol, stigmasterol, sitosterol, 5α-cholestanol, coprostanol, epicoprostanol and 24-ethylcoprostanol). In their model, human and carnivore fingerprints were closely linked relative to herbivore fingerprints, which were more similar to that of birds.
This lack of specificity could be the result of different factors. Firstly, goats are known to graze on a greater variety of plants compared to other herbivores  [63]  and as diet is a key factor in faecal stanol fingerprinting  [19] , the different diets inherent in different goat samples could partly explain their heterogeneity  [64]. Secondly, from a purely statistical point of view, this HCPC model and inherent clustering was built to maximize the explained variance between ten species, including omnivores with a very different stanol fingerprint compared to those of herbivores, and not to maximize the explained variance between particular species relevant to a specific context (i. e. including less species fingerprints). This lack of specificity of the PCA model to distinguish species fingerprints at a certain level highlights the importance of pre-screening when trying to apply this database to specific case studies in order to narrow the database used to only the species that have the potential to be present on a site  [10].
For ecologists, watershed managers, and soil forensic scientists, this prescreening step would necessarily involve a survey of local wild and domestic species, to make the database context-specific. Pre-screening has already been successfully applied to distinguish the main sources of faecal contamination in recent water and sediment studies  [23, 29, 27, 31]. This pre-screening study is more complex for archaeological studies since archaeological (faunal remains, artefacts) ethnographical (written and oral history), palynological (climate and vegetation reconstruction) or geochemical (elemental and isotopic analyses) clues are not necessarily available to identify the potential species present on site during its occupation period. Including too many species in the faecal HPCP model could lead to an enhanced and misleading variance to explain and artificially create a variance noise decreasing the efficiency of species distinction.
As more faecal reference samples from a larger number of mammalian species consuming different diets are added to our faecal reference library, species identification and the breadth of its applications will continue to improve. software (via FactoMineR and/or Rcmdr packages). R scripts are available on S4, S5, S6, S8 and S9 Tables. The authors are grateful for the constructive and useful comments of two anonymous reviewers which greatly contributed to improve the manuscript quality.
8 < R4 < 1. 2 = horse, [10] ). PCA-4 refers to the predictive PCA and its corresponding HCPC built with the distribution of the four main 5β-stanols (coprostanol, epicoprostanol, 24-ethylcoprostanol and 24-ethylepicoprostanol) in the human, dog, horse and reindeer reference samples from our database. PCA-11 refers to the predictive PCA and its corresponding HCPC built with the distribution of eleven 5β-stanols in the human, dog, horse and reindeer reference samples from our database. Reindeer and horses are herbivores so diagnostics between ratios and multivariate analyses can be compared.
PCA-4 refers to the predictive PCA and its corresponding HCPC built with the distribution of the four main 5β-stanols (coprostanol, epicoprostanol, 24-ethylcoprostanol and 24-ethylepicoprostanol) in the human, dog, lemming and reindeer reference samples from our database. PCA-11 refers to the predictive PCA and its corresponding HCPC built with the distribution of eleven 5β-stanols in the human, dog, lemming and reindeer reference samples from our database. Reindeer and horses are herbivores so diagnostics between ratios and multivariate analyses can be compared.
Acknowledgments We gratefully acknowledge the assistance of  Jasmine Ross ,  Robert Mayes  and  Marina Erb  in the lab,  Julia Kremkova  and  Gabriela Domene Lopez  in the field, and  Susan Ramsey  with the charcoal identification. We are also grateful to  Kjell-Åke Aronsson ,  Stine Barlindhaug ,  Anjessica Ericsson ,  Gillian Green ,  Timo Kumpula ,  Mari Kuoppamaa ,  Louis Lorant ,  Carol Kyle ,  Knut Røed , and an anonymous group of farmers close to  Vilhelmina (Sweden)  and for providing faecal reference samples. Thank you to  Benoit Geslin  and  Francois Husson  for advice on statistical analyses, and to  Paul Pettitt  for his comments on the manuscript. Competing interests: Authors declare no competing interest. Data and materials availability: For all samples, 5β-stanol relative abundances presented in the text are available in S2 Table as well as their summed concentration, concentrations of main 5α-stanols and recovery efficiency when a recovery standard was used.
01 < R3 < 0. 1 = cattle and horse, [21] ). R4 refers to the ratio of distributions of (24-ethylepicoprostanol/24-ethylcoprostanol) + (epicoprostanol/coprostanol) used to discriminate the horse fingerprint from other herbivores (No horse = 0. 8 < R4 < 1. 2 = horse, [10] ). PCA-4 refers to the predictive PCA and its corresponding HCPC built with the distribution of the four main 5β-stanols (coprostanol, epicoprostanol, 24-ethylcoprostanol and 24-ethylepicoprostanol) in the human, dog, horse and reindeer reference samples from our database. PCA-11 refers to the predictive PCA and its corresponding HCPC built with the distribution of eleven 5β-stanols in the human, dog, horse and reindeer reference samples from our database. Reindeer and horses are herbivores so diagnostics between ratios and multivariate analyses can be compared.
Acknowledgments. This study was conducted as part of a doctoral fellowship from the  Paris 1 University -Panthéon-Sorbonne. Analysis of the sherds was made possible by the  NSF  grant supporting the  Archaeometry Laboratory at the University of Missouri Research Reactor  (Number  1621158 ) and the  Proyecto Arqueológico Costa del Soconusco (PACS) , which was funded by the  U. S. National Science Foundation  ( BCS-1115361 ). Permission for destructive analysis was provided by the  Instituto Nacional de Antropología e Historia (INAH) de México. We also acknowledge the  French Ministère de l'Europe et des Affaires Étrangères (MEAE)  and the  CNRS (UMR 8096 ArchAm)  for their funding of the  Archaeological Uacúsecha Project , as well as the CEMCA for providing logistical support. We sincerely thank  Marie-Agnès Courty  for her invaluable assistance in the characterization of the paste groups.
Moreover, if the image sequence is sub-sampled, these parameters can substantially change between consecutive frames. The purpose of the present work is to develop a new straightforward method for image stabilization to overcome this limitation. This method provides a robust solution to stabilize an image sequence under varying illumination with the ability to process large databases. It builds on state-of-the-art techniques and available routines, with valuable input for the coastal imaging research community. The following sections will describe the basic steps to stabilizing an image sequence, compensating for camera motion to avoid positioning errors in the image. Furthermore, the image stabilization method will be applied to five years of continuous video data collected from a three-camera video system located at Anglet Beach in southwestern France.
( 3 ) For a higher up-sampling factor, instead of computing all of the up-sampled points of the array, only a small neighborhood around the initial peak estimate is up-sampled using a matrix-multiply discrete Fourier transform to refine the peak location. This dramatically reduces computation cost without losing accuracy. A detailed description of the algorithm is beyond the scope of the present contribution and can be found in  [53, 54]. In addition, a MATLAB code version of the algorithm is freely available and can be found in the MATLAB MathWorks File Exchange  [55]. Once the ∆u and ∆v corresponding to the cross-correlation peak are found, the displaced image can be relocated to the reference image position through an inverse transformation by mapping the pixel coordinates back to the reference image (f = T -1 g):    u v 1    =    1 0 -∆u 0 1 -∆v 0 0 1       u v 1    =    u -∆u v -∆v 1   .
When studying the nearshore zone, the vertical coordinate (z) is usually assigned to be equal to the instant sea level. However, if the topography is known and the swash zone is the subject of study (e. g. , wave runup), an iterative rectification method using the beach topography can be applied to reduce rectification positioning errors, as described by Blenkinsopp et al. [62]. Another popular calibration method is the one proposed by Holland et al. [20]. In their method, they solve the five intrinsic parameters in the lab and solve the six remaining extrinsic parameters using at least three GCPs and a standard nonlinear solver  [25, 26]. The intrinsic parameters can be estimated using the Caltech camera calibration toolbox for Matlab  [63]  prior to camera installation, in order to remove lens distortion.
Acknowledgments: The authors are thankful to  E. Bergsma  and  C. Bouvier  for their valuable comments on the method, and would also like to thank  Abril Jiménez  for kindly digitizing the shoreline position. Anglet beach is a monitoring site labelled by the  Service National d'Observation (SNO) Dynalit  (https://www. dynalit. fr). Storage resources for this study were provided by the computing facilities  MCIA (Mésocentre de Calcul Intensif Aquitain) of Univ. Bordeaux and Univ. Pau et Pays de l'Adour. The  OCA  ( Observatoire de la Côte Aquitaine ) has provided financial support to ensure the maintenance of the video station. The authors would like to thank the three anonymous reviewers for their constructive and positive feedback.
, who taught in Tchad from 1951 to 1958, while also investigating a village in the middle Logone valley for a DES that was supported by the  IEC. 51He continued to work on this region for his doctoral thesis, supported by  ORSTOM. 52He was then funded by the  CNRS  from  1960  to 1964 and subsequently taught at several universities in North Africa and France, as well as serving as the first rector of the  University of Tchad  (1971Tchad ( -1975)). These geographers shared similar working conditions and fieldwork experiences but they neither formed a school of 'tropicalists' nor used that adjective to describe their work. 53 Nonetheless, they produced an important body of research on the human geography of the various territories of French tropical Africa after mid century. Sometimes their enquiries converged around a common theme, despite having different starting points.
Results Gold ores mineralogy. Several methods were used to characterize the geochemistry and mineralogy of the gold ores, which included analyses of Pb, Cu and Ag isotopes, reflected light microscopy, scanning electron microscopy (SEM) and electron probe microanalysis (EPMA). The mineralogical results are summarized in Table  1  and the Pb, Cu and Ag isotopic compositions of ores (bulk analyses) are reported in Table  2. The label ' A' refers to the gold ore samples exploited by the ancient miners and the label 'M' to those of the modern mining sites (open pits/underground). Summarizing the data in Table  1 , the ore mineral paragenesis consists of native gold, sulfides (pyrite, chalcopyrite, stibnite, sphalerite, arsenopyrite, etc. ), and various sulfosalts (tetrahedrite, boulangerite, bournonite, etc.
Acknowledgements The authors thank the  Agence Nationale de la Recherche, France  (Grant  ANR-11-BSH3-0013 ) for covering the fieldwork, the geological studies and the financial support for MR and CGT. FA acknowledges support from the  European Research Council  grant  741454-SILVER-ERC-2016-ADG  for salary support at the time this manuscript was written. The  Centre National de la Recherche Scientifique  through its  Institut National des Sciences de l'Univers (INSU)  is warmly thanked. The  INSU funded and Ecole Normale Supérieure (ENS) de Lyon  operated ( ICP-MS) National Facility  is acknowledged for chemical and isotopic analyses. Thanks are addressed to  Thierry Aigouy  ( GET UMR 5563 , Toulouse) and  Philippe de Parseval  ( UMS 3623 -Centre de MicroCaractérisation Raimond Castaing, Toulouse ) for their help during SEM and EPMA analyses.