dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We would like to thank  M. Benbakoura  for his help in analyzing the light curves of several binary systems included in our set of stars. L. B. and R. A. G. acknowledge the support from  PLATO  and GOLF CNES grants. S. M. acknowledge support by the  National Aeronautics and Space Administration  under Grant  NNX15AF13G , by the  National Science Foundation  grant  AST-1411685  and the  Ramon y Cajal  fellowship number  RYC-2015-17697. E. C. is funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 664931. O. J. H and B. M. R. acknowledge the support of the  UK Science and Technology Facilities Council (STFC). Funding for the  Stellar Astrophysics Centre  is provided by the  Danish National Research Foundation  (Grant  DNRF106 ).
As organisms are adapted to their environments, assemblages of taxa can be used to describe environments in the present and in the past. Here, we use a data mining method, namely redescription mining, to discover and analyze patterns of association between large herbivorous mammals and their environments via their functional traits. We focus on functional properties of animal teeth, characterized using a recently developed dental trait scoring scheme. The teeth of herbivorous mammals serve as an interface to obtain energy from food, and are therefore expected to match the types of plant food available in their environment. Hence, dental traits are expected to carry a signal of environmental conditions. We analyze a global compilation of occurrences of large herbivorous mammals and of bioclimatic conditions.
, 2014). However, such models have a different objective: given environmental conditions, the goal is to estimate the likelihood of the presence, or the abundance, of species. In paleobiology and paleoecology modeling the focus is reversed: species distribution data are available and the goal is to reason about the physical environment. Canonical correspondence analysis of community composition  (Legendre and Legendre, 2012 ) constitutes yet another computational task, where the goal is to identify environmental gradients for species distributions directly from the occurrence data, without the proxy of functional traits. The goal of our study is to identify global patterns of association between dental traits of large herbivorous mammals and their environments in an ecometric way, that is, by analyzing dental trait distributions across communities.
The analysis uses a data mining approach called redescription mining  (Ramakrishnan et al. , 2004) , which in our study aims at describing geographic localities in terms of two alternative vocabularies: dental traits of occurring taxa on one hand and environmental conditions on the other. With this approach, we obtain a set of redescriptions ranked by accuracy, among which we identify common trends. We then analyze those trends across climatic zones. This approach differs from common ecometric modeling, where global models are constructed with the aim of accurately predicting climate based on the traits of species communities (see e. g. Liu et al. , 2012). Instead, redescription mining identifies associations between dental traits and climate which hold locally. The approach is conceptually similar to identifying biomes, that is, large ecological areas defined by abiotic factors such as climate, relief, geology and soils, hosting animals and plants adapted to their environment.
Data Sources In an ecometrics study is typically a site is the unit of analysis. Sites may correspond to physical places (e. g. national parks), ecologically defined regions (e. g. ecoregions), or geographic units (e. g. identified by placing a grid on a map). To extract ecometric patterns, we need to know environmental characteristics and trait distributions at each site. Our study builds on three datasets: taxa occurrence data at localities (Sites × Taxa), dental traits of taxa (Taxa × Traits) and climate variables at localities (Sites × Climate). Traits data are assigned at the species level, considering that the traits are assumed to be the same for all individuals within a species (Taxa × Traits). Therefore, this study does not require to observe or measure the traits of particular animals occurring at localities, only to know which species occur at which localities (Sites × Taxa).
Species occurrence data come from the list of the International Union for Conservation of Nature (IUCN, see https://www. iucn. org/). (Fortelius, 1981) , Kobus and Hyppotragus are from Figure  2  in  (Kaiser et al. , 2010) , all the other examples come from several illustrations in  (Thenius, 1989). of species occurrences and of climate variables (Sites × Taxa and Sites × Climate, respectively) have been compiled by M. Lawing and colleagues  (Lawing et al. , 2016) , communicated by J. Eronen. We used square grids of 50 by 50 km size as units of analysis, the finer resolution for the mammals occurrence and the climate available from the data sources. The occurrence data are based on ranges defined by the IUCN. Inherently, since animals are moving, occurrence ranges of large mammals can rarely be more precisely defined than in the order of tens of kilometres.
This resolution is sufficient for our purpose of identifying and analyzing global relations between dental traits of animal communities and climate. The dental traits dataset has been compiled by the authors (M. Fortelius and colleagues) and is available online at http://www. helsinki. fi/science/now/ecometrics. html. Most hypsodonty scores come from  (Liu et al. , 2012). The new traits data, together with all the reused datasets, the parameter settings for the mining algorithm and the algorithm outputs are available online at https://github. com/zliobaite/teeth-redescription. From the three data sources that we are using, the occurrence data are expected to be the most uncertain, since these data are based on expert inferred and observed ranges of occurrences. The precision of such data for large mammals is inherently limited to a scale of at least a few kilometers.
Data Aggregation Given the taxa occurrence data at localities (Sites × Taxa) and the dental traits of taxa (Taxa × Traits) we build the traits at sites dataset (Sites × Traits). Traits at sites can in principle be described by any descriptive characteristic of data distribution. Here we use the arithmetic mean. The original Functional Crown Type scoring scheme  ( Žliobait ė et al. , 2016)  has seven dental trait variables, five of which are binary and two (HYP and HOD) are ordinal, as described in Section 2. 1. We converted both ordinal variables into binary variables for individual species. In other words, we replaced variable HYP, which takes value 1, 2, or 3, by three binary variables Hyp1, Hyp2 and Hyp3, such that, for a given species, the new variable Hyp3 equals 1 if variable HYP took value 3 for the species and equals 0 otherwise.
Similarly for variables Hyp1 and Hyp2 with HYP values 1 and 2 respectively. We replaced variable HOD by three binary variables Hod1, Hod2 and Hod3 in the same way. Then, for each site and each trait, we took the mean of the binary trait variable over the taxa that occur at the site. Following this aggregation, we have a pair of datasets, (Sites × Traits) and (Sites × Climate), with matching sites characterized respectively by dental trait variables and climate variables. Two such datasets form the input of the redescription mining algorithm described in Section 3, which returns a collection of redescriptions, highlighting associations between the dental traits variables, the climate variables and sets of locations. FIGURE 2 summarizes the processes of aggregating taxa occurrences and dental traits over locations, and of extracting redescriptions.
The three initial datasets are represented by rectangles with a double border while the rectangle representing the aggregated dataset has a single border. The input of the redescription mining process is the pair of datasets (Sites × Traits) and (Sites × Climate). These datasets contain eleven dental traits variables (Hyp1 to CM) and nineteen bioclimatic variables (T ∼ Y to PColdQ) respectively (see TABLE  1 ). The variables are plotted on world maps in FIGURES 3, 4 and 5 (highdefinition zoomable versions of all the maps appearing in this paper are available online at https://github. com/ zliobaite/teeth-redescription). We discard locations with two taxa or fewer, considering that the data in such locations are too limited for the distribution of dental traits to be informative. This leaves us with 28887 locations, about 57% from the total 50365.
In addition to being accurate, the redescriptions should be statistically significant. In particular, for a given redescription R = (q a , q b ), we compute a p-value that indicates how likely it is that two subsets of entities X a and X b sampled independently with probabilities respectively pr(e ∈ X a ) = |supp(q a )| /n = p a and pr(e ∈ X b ) = |supp(q b )| /n = p b , where n denotes the total number of entities in the dataset, have an intersection as large or larger than the observed support of the redescription, supp(R). This p-value can be computed using the following formula: pV(R) = n k=|supp(R)| n k (p a p b ) k (1 -p a p b ) n-k. The redescription is deemed non-significant if the p-value is larger that some threshold (typically 0. 01 or 0. 05). As a practical example, consider the following query over bioclimatic variables: q C = [T ∼ WarmQ ≤ 18.
3] AND [T ∼ ColdQ ≤ 6]. We use Iverson bracket to specify satisfiability conditions, that is, in our case, the ranges in which the variables must take value. The query above selects sites where the value of T ∼ WarmQ is lower than 18. 3 and the value of T ∼ ColdQ is lower than 6. In other words, it selects sites where the temperature averages below 18. 3 • C during the warmest quarter and below 6 • C during the coldest quarter. Now, take the following, slightly more complex query over dental traits: q D = ([0. 75 ≤ OL] AND [CM ≤ 0]) OR [0. 8 ≤ Hyp3]. A site satisfies this query if among the taxa occurring there, either more than three-quarters have teeth with obtuse lophs and none has teeth with coronal cementum, or more than eighty percent are hypsodont. In the dataset used in this study, which contains 28887 sites in total, we find that 8590 sites satisfy this distribution of dental traits among taxa, while 7374 sites satisfy the climatic profile specified by q C , i.
Data Analysis Process Input. The input of the redescription mining process is the pair of datasets (Sites × Traits) and (Sites × Climate), or in more concrete terms two real-valued matrices of sizes respectively 28887 × 11 and 28887 × 19, as explained in Section 2. 3. All variables are listed in TABLE  1. Output. The output of the mining process is a collection of redescriptions, i. e. of pairs of queries, one over dental trait variables and one over climate variables respectively, each associated to the sets of locations satisfying the queries. We look for such pairs that have a high accuracy, i. e. a high Jaccard coefficient between the respective supports of the queries constituting the pair. Parameters. The mining process can be adjusted by tuning a range of parameters, described in more details in the tool's user guide (http://siren.
Disjunctions are more inclusive, since only one of the conditions needs to be true, for example: high percentage of hypsodont OR low percentage of flat teeth. As we strived for redescriptions that are reasonably easy to analyze and interpret, we did not allow disjunctions to appear simultaneously in both queries of a redescription. For the same reason, we also did not allow negations to appear in the queries. Computational analysis process. We mined redescriptions using the dental traits and climate variables described earlier. The goal of the experiment was to find what associations between dental trait distributions and climate are best supported in this dataset. We obtained 384 redescriptions in total, with accuracies ranging from 0. 68 down to 0. 03, and support sizes between 6694 and 289 locations.
Dental traits in China are similar to those of temperate zones, while the climate is most similar to that of the tropical zones covered by our redescriptions. The fact that the climate of this region, classified as Cfa in the K öppen system, is encountered only in few other locations worldwide and is hence fairly specific, further explains this lack of coverage. Our study is aimed at finding associations between dental traits and climate. The resulting redescriptions specify these relationships, how strongly they hold and where they hold geographically. While mechanically simple, applying the redescriptions to the past requires to carefully consider how to systematically evaluate the ability of the patterns to generalize to data coming from different sources and how to reconcile the diverging projections that may arise from different redescriptions.
The Gr component was fitted with a Doniach-Sunjic function to account for the metallic behaviour and the other components were fitted with a Voigt function. The Lorentzian widths were all set to 230 meV and the asymmetry parameter used for the graphene component was 0. 06. The values for the Gaussian width are 267 meV (Gr), 460 meV (SiC), and 1. 36 eV (BL). Photoelectron diffraction data were extracted from the intensity modulations of the graphene C 1s component (Gr) measured at different azimuthal and polar emission angles. The atomic plane arrangement of bilayer graphene with AB stacking is depicted in Fig. 4. The labelled polar angles of the near-neighbour interlayer forward focusing directions correspond to the 3. 354 Å interlayer spacing of bulk graphite, with in-plane C-C distances of 1.
Acknowledgements The authors acknowledge the support services of Synchrotron SOLEIL, especially the valuable help of Stephane Lorcy. The Synchrotron SOLEIL is supported by the  Centre National de la Recherche Scientifique (CNRS)  and the  Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA), France. DV, SG and XW acknowledge the financial support of the  Renatech network. This work was also funded by a public grant from the  French National Research Agency (ANR)  as part of the  "Investissements d' Avenir"  ( Reference  No. ANR-17-CE09-0016-05 ).
We matched the target sequences of published RT-PCR and isothermal amplification assays against an alignment of 61 complete YFV genomic sequences from GenBank (Table  1 ) to check the capability of each assay to detect all strains. Among the 14 assays described in international journals  [65] [66] [67] [68] [69] [70] [71] [72] [73] [74] [75] [76] [77] [78]  , most were clearly unsuitable for many strains owing to excessive mismatches between the primers and/or probe and the target genome. Four real-time RT-PCR assays, namely, two TaqMan  69, 70  , one LNA  75  and one SYBR Green-based assay  67  , were studied in detail and are predicted to detect all considered strains (Fig. 2 ). Similar in silico analyses of published isothermal amplification protocols predicted functional sets of primers and probes in two assays (Fig.
2 million years ago (midPliocene-eoi400). These climatic periods are well documented by palaeoclimatic and palaeoenvironmental records, with climate and environmental changes relevant for the study and projection of future climate changes. This paper describes the motivation for the choice of these periods and the design of the numerical experiments and database requests, with a focus on their novel features compared to the experiments performed in previous phases of PMIP and CMIP. It also outlines the analysis plan that takes advantage of the comparisons of the results across periods and across CMIP6 in collaboration with other MIPs.
g. Miller et al. , 2012). Model-data comparisons provide high confidence that mean surface temperature was warmer than pre-industrial temperature  (Dowsett et al. , 2012; Haywood et al. , 2013; Masson-Delmotte et al. , 2013). However, as is the case for the Last Interglacial, the PlioMIP simulations were not always performed using the same models that were used in PMIP3-CMIP5. The PMIP4-CMIP6 midPliocene-eoi400 experiment  (Haywood et al. , 2016)  is designed to elucidate the longterm response of the climate system to a concentration of atmospheric CO 2 close to the present one: 400 ppm (long-term climate sensitivity or Earth system sensitivity). It will also be used to assess the response of ocean circulation, Arctic sea ice, modes of climate variability (e. g. El Niño-Southern Oscillation), the global hydrological cycle, and regional monsoon systems to elevated concentrations of atmospheric CO 2.
Palaeoclimatic and palaeoenvironmental data for the PMIP4-CMIP6 periods The choice of the time periods for the PMIP4-CMIP6 simulations has been made bearing in mind the availability of palaeoenvironmental and/or palaeoclimate reconstructions that can be used for model evaluation and diagnosis. Past environmental and climatic changes are typically documented at specific sites, whether on land, in ocean sediments or in corals, or from ice cores. The evaluation of climate simulations such as those conducted for PMIP4-CMIP6 requires these palaeoclimatic and palaeoenvironmental data to be synthesized for specific time periods. A major challenge in building such syntheses is to synchronize the chronologies of the different records. There are many syntheses of information on past climates and environments.
hydrological cycle) across the PMIP ensemble, as well as for the recent climate. One further objective for the PMIP4-CMIP6 benchmarking will be to develop more process-oriented metrics, making use of the fact that palaeoclimatic data document different aspects of climate change. There are many aspects of the climate system that are difficult to measure directly, and which are therefore difficult to evaluate using traditional methods. The "emergent constraint" (e. g. Sherwood et al. , 2014)  concept, which is based on identifying a relationship with a more easily measurable variable, has been successfully used by the carbon-cycle and modern climate communities and holds great potential for the analysis of palaeoclimate simulations. Using multiple time periods to examine "emergent" constraints will ensure that they are robust across climate states.
A measure of the changes in key variables (Table  5 ) should be provided in order to assess remaining drift. Documentation should be provided via the ESDOC website and tools provided by CMIP6 (http://es-doc. org/) to facilitate communication with other CMIP6 MIPs. This documentation should also be provided for the PMIP4 website to facili-tate linkages with non-CMIP6 simulations. The PMIP4 special issue, shared between Geoscientific Model Development and Climate of the Past, provides a further opportunity for modelling groups to document specific aspects of their simulations. We also require the groups to document the spin-up phase of the simulations by saving a limited set of variables during this phase (Table  5 ). The data stored in the CMIP6 database should be representative of the equilibrium climates of the MH, LGM, LIG, and mPWP periods, and of the transient evolution of climate between 850 and 1849 CE for the past1000 simulations.
A minimum of 100 years' output is required for the equilibrium simulations but, given the increasing interest in analysing multi-decadal variability (e. g. Wittenberg, 2009) , modelling groups are encouraged to provide outputs for 500 years or more if possible. Daily values should also be provided and will allow the calendar issue to be accounted for (see  Appendix). The list of variables required to analyse the PMIP4-CMIP6 palaeoclimate experiments (https://wiki. lsce. ipsl. fr/ pmip3/doku. php/pmip3:wg:db:cmip6request) reflects plans for multi-time period analyses and for interactions with other CMIP6 MIPs. We have included relevant variables from the data requests of other MIPs, including the CFMIP-specific diagnostics on cloud forcing, as well as land surface, snow, ocean, sea ice, aerosol, carbon cycle, and ice sheet variables from LS3MIP, OMIP, SIMIP, AerChemMIP, C4MIP, and IS-MIP6 respectively.
Some of these variables are also required to diagnose how climate signals are recorded by palaeoclimatic sensors via models of tree growth  (Li et al. , 2014) , vegetation dynamics  (Prentice et al. , 2011) , or marine planktonic foraminifera (e. g. Lombard et al. , 2011; Kageyama et al. , 2013) , for example. The only set of variables defined specifically for PMIP are those describing oxygen isotopes in the climate system. Isotopes are widely used for palaeoclimatic reconstruction and are explicitly simulated in several models. We have asked that mean annual cycles of key variables are included in the PMIP4-CMIP6 data request for equilibrium simulations, as these proved exceptionally useful for analyses in PMIP3-CMIP5.
For the PMIP4-CMIP6 simulations, we strongly recommend that daily data are provided for the calculation of monthly or seasonal means, and so we include those in the PMIP4-CMIP6 data request for some key variables. Daily or 6-hourly data are also useful for running regional models. It is important to test the use of regional models for climate model projections at the regional scale. Regional models are also used to produce fine-scale palaeoclimate scenarios for use by the impact community, for example, to study past climate impacts on biodiversity via ecological niche modelling. Palaeoclimate indicators often respond to climate features not adequately captured with monthly data alone (such as growing season length). Daily weather variables are therefore required for some forward models, as well as for the computation of bioclimatic variables that are reconstructed from pollen data, for example (e.
Documentation and required model output for the PMIP4-CMIP6 database Detailed documentation of the PMIP4-CMIP6 simulations is required. This should include a description of the model and its components; information about the boundary conditions used, particularly when alternatives are allowed; information on the implementation of boundary conditions and forcings. Figures showing the land-sea mask, land-ice mask, and topography as implemented in a given model are useful for the lgm and midPliocene-eoi400 experiments, while figures showing insolation are particularly important for the midHolocene and lig127k experiments. Checklists for the implementation of simulations are provided in the PMIP4 papers, which give detailed information for each experiment; and.
Acknowledgements. Masa Kageyama  and  Qiong Zhang  acknowledge funding from  French-Swedish  project  GIWA. Pascale Braconnot ,  Johann Jungclaus , and  Sandy P. Harrison  acknowledge funding from  JPI-Belmont  project " PAleao-Constraints on Monsoon Evolution and Dynamics (PACMEDY) " through their respective national funding agencies. Sandy P. Harrison  also acknowledges funding from the  Australian Research Council  ( DP1201100343 ) and from the  European Research Council  for "GC2. 0:Unlocking the past for a clearer future". Alan M. Haywood  and  Aisling Dolan  acknowledge funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. 278636  and the  EPSRC-supported Past Earth Network. Ruza F. Ivanovic  is funded by a  NERC   Independent Research Fellowship  (no.
4. 3 3 Documentation and required model output for the PMIP4-CMIP6 database Detailed documentation of the PMIP4-CMIP6 simulations is required. This should include a description of the model and its components; information about the boundary conditions used, particularly when alternatives are allowed; information on the implementation of boundary conditions and forcings. Figures showing the land-sea mask, land-ice mask, and topography as implemented in a given model are useful for the lgm and midPliocene-eoi400 experiments, while figures showing insolation are particularly important for the midHolocene and lig127k experiments. Checklists for the implementation of simulations are provided in the PMIP4 papers, which give detailed information for each experiment; and.