dataesr/scientific-paragraphs-categorization huggingface dataset:
By comparison, rock state (weathering, folding, faulting, bedding etc. ), is rarely mentioned. This could be because the cliffs do not present any such characteristics, or because authors did not think it was relevant and did not mention it. Moreover, parameters describing rock state are either complex, technically expensive to describe and quantify, or outside the authors' scientific field of expertise. They were characterized with a Boolean value (True/False) to be integrated in the database. "True" refers to the presence of fracturing/weathering mentioned in the paper. "False" means that authors either describe fracturing/weathering as non existent/negligible or it is not mentioned in the paper.
CEREMA French national dataset The French CEREMA institute published a systematic national coastal cliff recession inventory  (Perherin et al. , 2012)  based on aerial photograph comparison every 200 m stretch of cliff along the entire French metropolitan coastline (1800 km of coastal rocky cliff, which corresponds to 465 (53 %) values in the database). This rich systematic dataset was obviously included in GlobR2C2 but with two caveats. On one hand, the CEREMA dataset introduces a strong spatial bias for French oceanographic and climatic conditions in the database observation records. This situation may risk polarizing the analytical results; however, this was recognized beforehand and specifically treated to prevent such bias (cf. Sect. 4. 2. 3). On the other hand, being a systematic study for every stretch of coastal cliff around the country makes it more robust to scientific and funding biases.
Research funds are often sought for areas combining coastal threats with societal interest. Therefore, coasts with higher recession rates are more often sampled, while quiet stretches of coastlines remain in the shadows. Consequently, including this data provides a more representative set of values existing along coastlines. Little studied sectors of the CEREMA research are hard rock coastal stretches (e. g. , hard proterozoic granites from French Brittany) and erosion rates lower than the study's detection threshold. Based on historical aerial photograph archives, CEREMA acknowledges that the quality of photographs limits the detectable cliff recession to rates higher than 10 cm yr -1. Below this value, they deem recession rates as undetermined. We chose to record those undetermined values in the database but not to use them in the statistical analysis.
Waves Wave properties were extracted from the ERA-interim reanalysis dataset  (Dee et al. , 2011). This gridded data has a pixel size of 0. 75 •. Temporally, data spacing is 6 h during the 1979-2016 period. Wave assault was characterized both in terms of mean agitation and extreme events. Three mean parameters characterize wave assailing force: significant wave height of combined swell and wind, wave period and wave direction. For swell characteristics, mean significant wave height and wave period characterize the average sea agitation. The wave direction value records the most frequent wave direction for the duration of the reanalysis period. Anticipating that mean sea state values may be deceptive metrics, a record of extreme events was also described. Those events were characterized by the 95th percentile of wave significant height as suggested by  Castelle et al.
Climate Climatic information was extracted from Climate Research Unit data between 1961 and 1990  (Mitchell and Jones, 2005). The grid size is 0. 5 • , at monthly time steps. Chosen parameters likely to influence erosion rate are mean annual rainfall, mean monthly temperatures and the number of freezing days (number of days per year below 0 • C). We did not find a global climatic dataset reporting time series of rainfall and temperatures spanning the durations covered by the articles contained in GlobR2C2.
Tidying the covariates: from database fields to predictors The first purpose of the database is to collate raw data from original sources in the most traceable manner possible. This data does not necessarily report information in an easily accessible fashion. This may be because (i) fields translate different realities (e. g. , recession rates vs. retreat values or recession rates relate to profile-specific recession rate or to kilometer long cliff sections), or (ii) value instances of a field are too broad and need summarizing in fewer categories (e. g. , lithology). Thus, post-processing was applied to the database in order to make it more homogeneous and more readily usable for statistical analysis.
Field unit conversion Original data may be provided in different ways (for example the time span between two measurements may be given by a duration or by start and end dates). As often as possible this information is summarized in a single duration field with a homogeneous unit. The following are the operations performed: -To obtain a duration in years, the fields measure duration (year), measure beginning and measure ending (date) were merged together. -Retreat (m) and eroded volume (m 3 ) were converted to retreat rate (m yr -1 ). -The mean cliff height was either obtained from a cliff height mean field or as the mean between height min, height max (m). -The error (m yr -1 ) was a compilation of the error value and error type.
Rock resistance inference The database, filled with information from publications, results in more than 40 distinct lithological descriptions. We first grouped lithology into 9 groups with a similar classification to that of  Woodroffe (2002)  for historical comparison. But lithology alone does not govern rock mass mechanical properties. Tectonic inheritance, deformation, fracturing and weathering weaken the rock masses. Consequently, the rock constituting the cliffs are divided by rock mass strength criteria. Following the practical examples from  Hoek and Brown (1997) , we propose to further aggregate Hoek and Brown's macroscopic rock mass strength categories into three categories. Hoek and Brown (1997)   Aggregation criteria are based on the fields lithology name, weathering, fracturing and comments, in which all published details on rock strength, structural geology, weath-ering were preserved.
Database content and completeness The database is filled with 58 studies, which is comprised of 47 peer-reviewed articles and 11 public national databases, documenting 1530 cliff sites and 1680 erosion rate records. Indeed, some cliff sites were repeatedly measured over different periods. With more than 90 % of fields complete, the database is satisfactorily thorough; however, the constitution of the database highlights some characteristics that are often poorly reported. We previously mentioned the difficulty regarding finding a description of cliff rock weathering and fracturing. Those fields are missing for 98. 4 % of the records (corresponding to 53 publications).
How was erosion measured? The number of studies has steadily been growing since the mid-1990s (Fig. 5 ), for every method type. Older studies exist and are present in Sunamura's database, although those papers were not available and/or cliff and measurement descriptions were too poor to be encoded in our database. The most commonly used method is the comparison of aerial photographs or historic maps, which correspond to an easy to apply 2-D method and allow for erosion evaluation spanning several decades. Forty-three studies used this method, which represents 50 % of the published studies and 88 % of the records. The second most used method is 3-D techniques, which have become common since the mid-2000s. This method represented 19 studies (22 % of the published studies) and 5 % of records.
Erosion vs. marine forcings In order to explore the influence of sea aggression, several variables were implemented in the database describing mean sea agitation and tidal range, and sea agitation during extreme events. All the variables concerning swell are strongly correlated. Hence, only three independent marine parameters are analyzed in the scatterplots in Fig. 7 : tidal range, wave energy flux and the number of storms. All scatterplots appear to be widely spread and do not show simple linear relations. Indeed, the Spearman rank correlation coefficients, which evaluate monotonic relations between two variables, are low (Fig. 8 ). Furthermore, many tentative correlations cannot be trusted (p value > 0. 05). These correlations and the associated p values are given in Fig. 8. Exploration of marine forcings indicate that no forcings have an apparent effect on erosion rates; the exception to this finding is a weak relationship between tidal range and erosion rates, which suggests higher erosion for tidal ranges between 1 and 3 m (although this is not visible for medium resistant rocks).
Erosion vs. climatic forcings Concerning climatic forcings, recession rates are compared to temperature variation, frost frequency and the amount of rainfall. As for marine forcings, data is very scattered (Fig. 9 ). Frost day frequency and rainfall show a positive trend with erosion rate for weak resistance rocks. Poorly consolidated rocks represent the large majority of rock types present in cold (> 50 frost day per year) and rainy climates (> 1000 mm yr -1 ) in the database. Only a few studies concern harder rocks in cold climates. However, even if a trend exists, data are widely distributed and the Spearman rank correlation coefficient is low (0. 25 for frost and 0. 07 for rainfall). Mean annual temperature does not show any clear correlation with erosion rate.
Comparison to previous studies The GlobR2C2 database provides a quantitative overview of the current coastal rocky cliff erosion knowledge. This database is the first update since Sunamura's 1992 seminal publication and adds 54 additional quantitative studies to the scientific debate. Its design allows for an assessment of the drivers of erosion. Historically,  Woodroffe (2002)  has already tried linking erosion with lithology in a broadly reproduced graphic. This graph shows a clear pattern of increasing erosion rates with decreasing rock resistance. GlobR2C2 updates this classic graph using the same lithological classification (Fig. 10 ). New knowledge does not change historical views; however, it narrows the assumed erosion rate ranges down, both towards lower and higher rates. We also observe that supposed hard rocks such as granites or basalts can erode as quickly as 1 m yr -1.
What knowledge does GlobR2C2 compile? The GlobR2C2 database is based on bibliographic references as well as models and reanalysis, which are used as proxies for forcings; some biases are inherent to this kind of approach. The next paragraphs focus on different aspects of these limitations due to (i) the use of cliff retreat rate as a proxy of erosion, (ii) the use of models and reanalyses as proxies of forcing and (iii) the use of peer-reviewed journals. The overprinted lines on the scatterplots represent moving median, and the numbers are the Spearman rank correlation coefficients, which were only reported when the p value was significant (> 2e -2 ).
Forcing proxies While publication-derived cliff recession rates and cliff conditions could be forced into a coherent database framework, environmental forcings were so scarcely and heterogeneously documented that the same rationalization process was not possible on the basis of publication alone. Instead, publicly available global climatic and sea condition databases were used. These databases present the advantage of being spatially and temporally continuous thanks to reanalyzed climate and sea state models. Their principal limitation is their coarse-grained definition compared to site specificities. Nevertheless, they document external forcings (i) in a uniform fashion (regular spatial and temporal sampling steps), (ii) for the entire globe and (iii) reflect forcing condition for durations spanning several decades.
Literature biases as future tracks to improve cliff evolution understanding GlobR2C2's worldwide compilation shows that research in this domain is very active. A large body of quantitative data already exist. However, even if data coverage is somewhat global, publications have been found to focus primarily on a few western countries. This finding also reflects the strategy of literature search adopted: only international and national literature published in English, French or Spanish were compiled. Due to the language barrier, we are aware that studies in Russian, German or Japanese, among other languages, were unwillingly omitted. Spatially, our search strategy did not flag scientific literature on the evolution of African and South American cliffs. Cliff recession studies appears to be focused on the richest areas where economically valuable coastal assets are exposed to losses.
Conclusions Compared to continental cliffs, coastal cliffs obviously erode more quickly due to the presence of the sea. The GlobR2C2 v1. 0 database compiles ca. 2000 coastal rocky cliff retreat data from an online global literature search published before 2016. It is the first attempt of its kind since Sunamura's seminal publication in 1992. The investigated period adds information arising from the quantitative revolution of lidar technology and the use of the structure from motion (SfM) technique, which is accessible to scientists with little background in photogrammetry, in addition to the massive release of aerial photographic archives from mapping agencies in western countries. The data compiled in GlobR2C2 is heterogeneously distributed in terms of retreat rates, geographical location, cliff nature and climate settings.
Together with cliff settings compiled from publications, GlobR2C2 also records continental climate and marine conditions at study sites from reanalyzed models for their global, spatial and temporal sampling regularity. Both forcings exhibit a weak relationship with cliff recession rates. However, in relative terms, climate (i. e. , frost days frequency) exhibits a stronger influence than marine forcing. The influence of the sea is only slightly visible in this dataset through the maximum efficiency of erosion for tidal ranges between 1 and 3 m. Our data divides rocky coasts into three classes of resistance, following the Hoek and Brown parameter. The most resistant (least resistant) rocks are found to lead to retreat rates of less than 10 cm yr -1 (83 % quantile), whilst the least resistant rocks are found to lead to retreat rates of up to 85 cm yr -1.
Acknowledgements. Mélody Prémaillon 's PhD fellowship was funded in equal part by  BRGM  (the  French geological survey)  and the  French region Midi-Pyrénées. Laurent Roblou  and  Dominique Astruc  are warmly thanked for advice about tide calculation and wave power, respectively. We thank  Sebastien Carretier ,  Christophe Garnier ,  Elise Nardin  and  Delphine Rouby  for their support and wise advice during committee meetings. Edited by:  Mary Bourke Reviewed  by:  Larissa Naylor  and Cherith Moses.
Model evaluation The base case simulation of each model has been evaluated on a monthly mean basis using available surface observations from Europe and North America. The observational data used in this study are the same as the dataset used in the second phase of AQMEII  (Im et al. , 2015a, b)  The model evaluation results for each model are presented in Figs. 1  and 2 , and in Table  3 , along with the results for the multi-model (MM) mean and median values. The results show that the monthly variations of gaseous pollutants are well captured by all models with correlation coefficients (r) generally higher than 0. 70. The biases in simulated O 3 levels are generally less than 10 % with a few exceptions of up to -35 %. The temporal variations of NO 2 levels are also well simulated (r > 0. 7) but exhibit much higher biases, with underestimations up to 75 %.
Acknowledgements. We gratefully acknowledge the contribution of various groups to the third  Air Quality Model Evaluation International Initiative  (AQMEII) activity. The  Joint Research Centre Ispra/Institute for Environment and Sustainability  provided its ENSEMBLE system for model output harmonization and analyses and evaluation. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the  U. S. Environmental Protection Agency. Aarhus University  gratefully acknowledges the  NordicWelfAir  project funded by the  NordForsk's Nordic Programme on Health and Welfare  (grant agreement no. 75007 ), the  REEEM  project funded by the  H2020-LCE Research and Innovation Action  (grant agreement no. 691739 ) and the  Danish Centre for Environment and Energy (AU-DCE).
Methods For GBD 2015, diseases were defined according to the International Classification of Diseases, 10th revision (ICD-10), and organised in a hierarchical classification  [6, 7]. The first level of this classification comprises three main disease groups: communicable diseases (group 1), non-communicable diseases (group 2), and injuries (group 3). These three groups are divided in 21 categories. The communicable disease categories include, for example, infectious and parasitic diseases and neonatal conditions. Categories of non-communicable diseases include mental disorders, as well as malignant neoplasms and endocrine, blood, and immune disorders. Injuries regroup intentional and non-intentional injuries. The third level of classification is closer to the usual ICD-10 categories. Mental disorders, for instance, are divided into 13 subcategories: major depressive disorders, dysthymia, bipolar disorders, schizophrenia, alcohol use disorders, drug use disorders, anxiety disorders, eating disorders, autism spectrum disorders (reported in the GBD database as Autism and Asperger syndrome), conduct disorders, attention-deficit/hyperactivity disorder (ADHD), idiopathic intellectual disability, and other mental and behavioural disorders.
The aggregate data from the 2015 GBD study are freely available  [7]. This dataset provides DALYs for the six WHO regions-Africa (AFR), the Americas region (AMR), South-East Asia (SEAR), Europe (EUR), the Eastern Mediterranean region (EMR), and the West Pacific region (WPR)-for each sex and for seven age groups: < 28 days, 1-59 months, 5-14, 15-29, 30-49, 50-69 years, and 70 and older  [6]. Formally, DALYs are the sum of years of life lost (YLLs) and years lost due to disability (YLDs) for disorder (d), age (a), sex (s), and year (t). YLLs and YLDs are estimated as follows: In the GBD 2015 study, disability weights were obtained from two international surveys in the general populations of nine countries in 2011 and 2013: Bangladesh, Indonesia, Peru, Tanzania, the USA, Hungary, Italy, the Netherlands and Sweden  [8, 9].
Body mass has been intensively studied in a wide range of vertebrates because of its marked influence on both survival and reproductive performance  [27, 28]  and, more generally, population dynamics  [29] [30] [31]. We analyzed body mass data collected from two long-term individual-based studies of populations living in very different ecological contexts. The roe deer population at Chizé, in Western France, experiences large inter-annual variation in habitat quality, especially during the critical fawn rearing period in spring and summer  [32]. By contrast, roe deer at Bogesund, in South-eastern Sweden, have access to rich and rather more predictable resources during early life. We investigated how variance components of juvenile body mass were associated with yearly variation in earlylife conditions at Chizé where substantial cohort effects have previously been documented  [33].
Study sites and data collection The study used data collected from populations of roe deer that differ markedly in terms of environmental conditions. The first population inhabits a 2614 ha enclosed forest in western France, the Réserve Biologique Intégrale of Chizé (GPS coordinates: 46°11'N;-0°34'W). Chizé has an oceanic climate with some Mediterranean influence, with mild winters and warm, often dry, summers. The primary production of this forest is quite low due to poor soils, and the availability of food for roe deer is limited by summer drought. Previous work has shown that the demographic performance of roe deer in Chizé is strongly limited by a combination of harsh climatic events and a relatively high population density in certain years  [39]  which depresses both juvenile body mass  [36]  and fawn survival  [32].
Acknowledgments We are grateful to  Johan Månsson ,  Jonas Nordström ,  Lennart Pettersson ,  Leif Rooth ,  Gunnar Schön  and  Pia-Lena Widén  for doing most of the field work in Sweden, and to  Britany Amblard  for her help during the microsatellite genotyping. We are grateful to Zoodyssée staff (Chizé) and many volunteers who contributed to roe deer captures in France. We also thank  L. Kruuk ,  J. F. Lemaître ,  A. Charmantier  and  C. Walling  for fruitful discussion, and two anonymous reviewers for insightful comments on a previous version.
Funding The  Bogesund study in Sweden is part of a long-term study of population demography in roe deer , previously supervised by  Olof Liberg  and currently by PK, both at  SLU, Grimsö, Sweden. The data collection at Bogesund was financed by the  Swedish Association for Hunting and Wildlife Management  and by the private foundations of " Oscar och Lilli Lamm stiftelser", "Olle och Signhild Engkvist stiftelser " and FORMAS. The monitoring of the Chizé population is performed and funded by the  Office National de la Chasse et de la Faune Sauvage (ONCFS). Genetic data used in this work were produced through molecular genetic analysis technical facilities of the  labex "Centre Méditerranéen de l'Environnement et de la Biodiversité " thanks to the support of the ' PATCH '  RPDOC ANR  project ( ANR-12-PDOC-0017-01 ) awarded to C.
In this paper, we investigate the uniformity of French and Indian War (1754-1763) soldiers' diets, and assess the influence of local infrastructure in provisioning frontier forts. To accomplish this analysis, the animal bone assemblage recovered from recent excavations at Fort Shirley, a French and Indian War frontier fort on the Aughwick creek in central Pennsylvania, is compared to published datasets from ten other French and Indian War fortifications in Pennsylvania, West Virginia, Maryland, Michigan, Quebec, and Tennessee. Where rivers, roads, and local suppliers were available, standardized provisioning likely resulted in the domesticate-based diets described in historic documents. However, frontier fortifications were also erected in areas lacking these key components. We hypothesize that comparison of faunal data from frontier fortifications like Fort Shirley, which was located far from established European communities and predated the development of road systems in the area, with those associated with road systems and urban centers will reveal significant differences in individual sites' access to, and reliance on, standard military provisions  (Sullivan 1997).
Comparative Sample Faunal data from additional French and Indian War fortifications were assembled from published and unpublished reports for comparison (Table  2 , Figure  2 ). Attempts were made to make this dataset as large as possible; however, only French and Indian War sites with complete lists of identified species were included. Where possible, additional criteria were used to further refine the dataset for this analysis. For example, only the material attributed to the permanent occupation of Fort Loudoun (TN) was used, since this is believed to more accurately represent the diet of soldiers dependent upon the state of local infrastructure than those present during the site's construction who brought provisions and equipment to the site with them  (Breitburg 1983). Similarly, though a series of analyses have been performed at Fort Michilimackinac (e.