dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This paper is dedicated to the memory of our dear lecturer and mentor  L. Gerardo Vega Toscano  (PdlP and FC). PdlP acknowledges a  Pouroulis Family Foundation Fellowship in African Quaternary Archaeology  and  Hominin Palaeoecology  at the  University of Cambridge , and the action  PID2019-1049449GB-I00  funded by the  Spanish FEDER/Ministry of Science and Innovation. PdlP is grateful for the help and advice of  Carlos Rodríguez -Rell an. LB was funded by a  National Geographic   Explorer grant  ( NGS-54810R-19 ) and  DSI-NRF Centre of Excellence in Palaeosciences (Genus)  grant ( CEOOP2020-1 ). FdE was supported by the  European Research Council (ERC)  under the  Horizon 2020 program  ( QUANTA  project, contract no. 951388 ), the  Research Council of Norway through its Centres of Excellence funding scheme, SFF Centre for Early Sapiens Behaviour (SapienCE) , project number  262618 , the  LaScArBx research programme  ( ANR-10-LABX-52 ), the  Talents Programme  [grant number:  191022_001 ], and the  Grand Programme de Recherche 'Human Past' of the Initiative d'Excellence (IdEx) of the University of Bordeaux.
Extensive data pertaining to the main isotopolog were obtained more recently between 260 and 360 GHz  [35]  and between 15 and 73 cm -1  [36]. Results of a millimeterwave and far-infrared study on c-C 2 H 3 DO were reported very recently  [37]. Unlabeled atoms in formulae designate 12 C and 16 O here and in the following. We also mention a high-resolution IR study  [38]  and intensity measurements on c-C 2 H 4 O  [39]. Our present work describes the investigations of c-13 CCH 4 O and c-C 2 H 4 18 O employing samples of oxirane in natural isotopic composition to facilitate their detection in space. We also measured transition frequencies of the main isotopic species mainly to test the performances of Watson's S and A reductions of the rotational Hamiltonian in combination with an oblate and a prolate representation.
Observed spectrum and determination of spectro - scopic parameters of c-C 2 H 4 O The initial calculation of the rotational spectrum of c-C 2 H 4 O was based on the second version of the Cologne Database for Molecular spectroscopy, CDMS,  [46, 47, 48]  entry of the oxirane main isotopic species from July 2012, which, in turn, is based on the FIR study from that year  [36]. This study employed Watson's A reduced Hamiltonian in the prolate I r representation, as was done in the first version of the c-C 2 H 4 O CDMS catalog entry of March 2000, on which the FIR study built. The same combination of reduction and representation was used in an IR study  [38] , whereas Pan et al. employed Watson's A reduction in the oblate III l representation using 25 spectroscopic parameters in their fit  [35].
The FIR rms was reduced to 0. 00009 cm -1 after these two corrections, and we assigned 0. 00010 cm -1 as uncertainties to the FIR transition frequencies. The assignments of four more transition frequencies were extended eventually because more individual transitions contributed substantially to each measured line than was indicated in the initial line list  [36] ; further details are available in our line list which is part of the Supplementary Material. These corrections, however, had only very small effects on the fit. It is important in this context to emphasize that in the case of two or more overlapping transitions, our fitting program SPFIT only judges the intensity-weighted average position of all overlapping transitions. One set of MW transition frequencies  [34]  was reportedly accurate to 0.
01 MHz, but several lines displayed larger residuals between calculated and measured frequencies. In fact, the rms of this data set was very close to 0. 02 MHz, and we attributed this value to this set of transition frequencies. Uncertainties of 0. 05 MHz and 0. 15 MHz, respectively, were employed for the remaining datasets  [33, 35] , as was done earlier  [36]. Spectroscopic parameters were determined for this as well as various intermediate line lists using the A reduction in the I r representation and employing the S reduction in the III l and I r representations. The A reduction in the III l representation was only used for the final line list as this combination of reduction and representation appeared to require many more spectroscopic parameters than the other combinations. Pickett's SPFIT program  [49]  was applied for all fitting, and the SPCAT program was utilized for calculations of all rotational spectra.
Acknowledgments It is our great pleasure to dedicate this work to the memory of  J. K. G. Watson  for his many and invaluable contributions to the theory of high-resolution spectroscopy. We will also keep him in mind for his great sense of humor and for his Scottish accent. We thank  Arnaud Belloche  for communicating results of his search for c- 13 CCH 4 O in astronomical data. The work in Köln was supported by the  Deutsche Forschungsgemeinschaft  through the collaborative research center  SFB 956  (project  ID  184018867 )  project  B3  and through the  Gerätezentrum   SCHL 341/15-1  (" Cologne Center for Terahertz Spectroscopy "). J. -C. G. acknowledges support by the  Centre National d'Etudes Spatiales  (CNES; grant number  4500065585 ) and by the  Programme National Physique et Chimie du Milieu Interstellaire (PCMI)  of  CNRS/INSU  with  INC/INP  co-funded by  CEA  and  CNES.
4 nm and 3. 3 nm for Ag 0. 50 Al 0. 50 and Ag 0. 25 Al 0. 75 , respectively), suggesting that they also contain some aluminium. Therefore, we expect that aluminium is not completely oxidized within the BNPs. XEDS spectra were acquired on assemblies of Ag x Al 1-x BNPs and the estimated relative atomic percentages were found to be consistent, within in the uncertainties of the measurements, with the proportions given by the supplier of the initial alloy targets used in our laser vaporization source. Further analysis of the XEDS data obtained from individual BNPs did not yield interpretable variations in composition between particles. The low X-ray count rate intrinsic to the small volume of the particles in this study results in non-negligible relative errors for each measurement. Unfortunately, increasing the acquisition time of individual spectra to gain more counts results in the electron beam induced destruction of the particle being measured  [35].
The authors declare no competing financial interests. The samples were elaborated at the PLYRA facility with technical support from  C. Albin ,  O. Boisron ,  C. Clavier  and  S. Hermelin  who are warmly thanked. The authors also gratefully acknowledge the  Centre Technologique des Microstructures (CTm)  and especially  Xavier Jaurand  for his availability and technical expertise. The authors also thank the  Consortium Lyon Saint-Etienne de Microscopie (CLYM)  for access to the  FEI Titan ETEM. The authors acknowledge TEM access provided by the  METSA network. The authors would like to acknowledge support from  GDR 3182 Nanoalliages, European COST   Action MP1302 Nanospectroscopy and the Nanoalloys International Research Network IRN CNRS "Nanoalloys ". This work was also partially financed by the  French National Research Agency (Agence Nationale de Recherche, ANR)  via the project ' SchNAPSS ',  ANR-21-CE09-0021.
, 1986) ; (b) their bed contains more likely younger and mechanically weaker lithologies than hard beds  (Jiskoot et al. , 1998 (Jiskoot et al. , , 2000)) ; (c) they are clustered in climatic envelopes between cold-dry and warm-humid environments  (Sevestre & Benn, 2015) ; and (d) they are more likely polythermal in Svalbard (only region where the thermal regime has been integrated into a statistical study)  (Jiskoot et al. , 2000). Based upon these studies,  Sevestre and Benn (2015)  built an entropy maximization model to qualitatively classify the glaciers in the Randolph Glacier Inventory database, RGI (2017), into five surging categories, from no surge to surge-type (Figure  1 ). However, statistical studies of glacier surges have two limitations: (a) they use integrated features for entire glaciers, and (b) none of them are comparing different type of models.
We propose a framework to regularize the evaluation of several machine learning models for determining glacier surge probability. The machine learning framework aims at identifying glacier areas that might enter an unstable regime due to their geometrical and climatic configuration (Figure  2 ). Selecting the best performing model, the Extreme Gradient Boosting (XGBoost)  (Chen & Guestrin, 2016) , we identify the features that control the classification of surge-type glaciers. By applying this framework on a custom-built database, we produce a map of surge probability for Svalbard glaciers. Using this model, we demonstrate that geometrical features have a high impact on the classification, and these findings are discussed in the context of the existing glacier surge theories. The machine-learning framework can be applied for assessing the surge probability of glaciers in other regions of the world, expanded when new data are available and/or adapted to other fields (e.
Data and Methods to Assess the Surge Probability of Glaciers in Svalbard We develop a machine learning framework for classifying surge-type glaciers. This framework includes the development of a custom-built database, a method for training machine learning models consistent with best machine learning practices, methods for evaluating the model outputs (i. e. , the probability for a glacier to be classified as surge-type), and finally a method for mapping the surge probability of Svalbard glaciers. Additionally, we identify the key features that control the predictions of the models. We build a glacier database by combining the Randolph Glacier Inventory (RGI, 2017), geometrical features  (Fürst et al. , 2018; Maussion et al. , 2019) , and climatic data  (Pelt et al. , 2019). These data are discretized along the glacier centerlines.
After discretizing and post-processing the data, the custom-build database combines 981 glaciers which are discretized along 97,140 points over Svalbard. The database is used in three different supervised machine learning models: logistic regression, random forest, and XGBoost. Data are split between training and testing data-sets. Training data are used to teach the machine learning models whether a glacier is surge-type. Testing data are used to evaluate the ability of the models to classify surge-type glaciers. (RGI, 2017). This database contains five classes that characterize the surge potential of glaciers  (Sevestre & Benn, 2015) : Not observed (0), Possible (1), Probable (2), Observed (3), Not assigned (9). The class 9 is not represented in the Svalbard region. These models are evaluated using multiple statistic metrics, such as the area under the Receiver Operator Characteristic curve  (Hanley & McNeil, 1982).
Randolph Glacier Inventory Features The Randolph Glacier Inventory (RGI, 2017) is a globally complete database of digital outlines of glaciers worldwide, excluding the ice sheets. This database was developed to provide better estimates of past and future surface mass balance of glaciers  (Pfeffer et al. , 2014). It includes integrated features such as glacier surface area and length. Glaciers are classified into five different surging categories: 0 -Surge not observed, 1 -Possible surge-type, 2 -Probable surge-type, 3 -Surge Observed, 9 -Not surging (Figure  1 ). This classification has been established following the work of  Sevestre and Benn (2015). While the classes 0 and 3 are based on field observations, the classes 1 and 2 are based on statistical modeling  (Sevestre & Benn, 2015). However, no quantitative predictions of surge probability are assessed.
The Randolph Glacier Inventory is distributed through the Global Land Ice Measurements from Space, and the National Snow and Ice Data Center (GLIMS/NSIDC) website  (RGI, 2017). It is continuously developed and new versions are released regularly. In the present study, we use the most recent version (v6. 0) for the Svalbard region, which is the region 7 in this database. From the Randolph Glacier Inventory, we use only the unique identifier allocated for each glacier in Svalbard (RGIId), the corresponding glacier name and the surging class (Figure  3 ). Other features present in the Randolph Glacier Inventory, such as the surface area and the length of glaciers, are not used because these are integrated features across each glaciers while we focus in this study on discretized variables along glacier centerlines.
, 2019). The geometrical widths are computed by intersecting lines perpendicular to the flow lines at each grid point with the glacier outlines and the tributary catchment areas. The detailed workflow is described in  Maussion et al. (2019). The bed elevation and glacier thickness are retrieved from  Fürst et al. (2018). These authors presented a first version of the ice-free topography (SVIFT1. 0), which was computed using a mass conservation approach for mapping glacier ice thickness. This database is built from more than one million point measurements coming from radio-echo soundings. In total, it corresponds to an accumulated length of 700 km of measured thickness profiles. The ice thickness was also computed where measurements were not available and an error estimation map is available by comparing the model calculation and the observations.
Climatic Features We added climatic features to the database, that is, runoff and climatic mass balance (CMB). Pelt et al. (2019 Pelt et al. ( ) created a long-term (1957 Pelt et al. ( -2018) )  data set of CMB for the glaciers, snow conditions, and runoff with a 1 × 1 km spatial resolution and 3-hr temporal resolution over Svalbard. These authors used a coupled energy balancesubsurface model, forced with down-scaled regional climate model fields, and apply it to both glacier-covered and land areas in Svalbard. In our study, we characterize CMB by spatially distributed values of the Equilibrium Line Altitude (ELA) and mass balance gradient. The runoff is the local discharge corresponding to the available water coming from rainfall and melt at the bed after accounting for retention by refreezing and liquid water storage  (Pelt et al.
Custom-Built Database of Svalbard Glaciers The database is the combination of all the features discretized along the centerlines. Since the climatic data have a spatial resolution of 1 × 1 km, we exclude all the glaciers with a surface area less than 1 km 2 and a length less than one km. As a consequence, our custom-built database contains 981 glaciers which are discretized along 97,140 points: 70,937 points belong to the class "Not Observed Surging," 10,598 belong to the class "Possible Surge," 4,361 belong to the class"Probable Surge-type," and 11,244 belong to the class "Observed Surging. " The surge class of an entire glacier is assigned to every points discretized along the centerline of this glacier. Eleven features are used to train the statistical models: the surging class (1), the bed elevation and slope  (2, 3) , the surface elevation and slope (4, 5), the thickness (6), the CMB (7), the glacier width (8), the width divided by the thickness (9), and the driving stress (10).
A random number is also added as a dummy feature (11) that does not have a physical interpretation and is used here to compare the importance of the other features to a random value  (McBeck et al. , 2020). The final database contains as well the Randolph Glacier Inventory identifier and the corresponding glacier name. Figure  4  displays the correlations between these features. The features clustered in the upper left corner of the correlation matrix show high positive or negative correlations. Following the diagonal of the matrix toward the lower right corner, the correlations are decreasing. The bed elevation, thickness, width, runoff, bed and surface slope are highly correlated with each other. The driving stress, width times thickness (W × H), and the dummy features show correlation values close to 0, indicating that they are not correlated to other features.
Therefore, we under-sampled the Not-Observed surge glaciers such that the data set contains a 50%-50% distribution of non-surging and surging glaciers. This data set is then split into a training and a testing set, respectively 70% and 30% of the database. We justify this split by running ten different models trained with a split between 10% and 90% for the training data set. The values of the Area Under The Curve (AUC) is acceptable when 70% of the database is used for training, while higher proportion might lead to overfitting model. A precise description of the analysis is presented in the appendix (Appendix A, Figure  A1 ).
Random Forest Random forest is a tree-based ensemble machine learning technique that is constructed by a multitude of decision trees. Each tree in the random forests is producing a class prediction and the class with the most votes becomes the model prediction  (Breiman, 1999). We implemented the random forest models with the scikit-learn library of Python  (Pedregosa et al. , 2011) , and using the Gini impurity: Gini = 𝐶𝐶 ∑ 𝑖𝑖=1 𝑓𝑓𝑖𝑖 (1 -𝑓𝑓𝑖𝑖) (2) where f i is the frequency of the label i at a node and C is the number of unique labels. We used 1000 trees in the forests with a maximum depth of 2 and the number of features to consider when looking at the best split is the square root of the number of features (Appendix C, Table  C1 ).
We use decision trees as the base learners. The gradient boosted equation is formulated as follows: log 𝑝𝑝 1 -𝑝𝑝 = 𝐹𝐹0 + 𝑀𝑀 ∑ 𝑚𝑚=1 𝑟𝑟𝑚𝑚(𝐗𝐗)𝐹𝐹𝑚𝑚(𝐗𝐗) ( 3 ) where m is the iteration index over M maximum iterations. F m (X) is the current iteration fitted to the previous iterations residuals r m (X). F 0 is the base estimate. We implemented XGBoost using the xgboost library in Python  (Chen & Guestrin, 2016). The objective is the logistic regression, we define 20,000 boosting learners, trees have a maximum depth of 2, and the minimum child weight is 1 (Appendix C, Table  C1 ).
The AUC is consistent using different training data sets picked randomly and stays stable when we perform the K-Fold cross validation. XGBoost is the best performing model in this study and its performance is stable regardless of the points used to train the models. Because of the stability of the performance, the final results are not computed using the K-Fold cross validation since it will not improve tremendously the performance of the model and we choose to establish the model stability as presented in Appendix B.
Surge Probability Map of Svalbard Glaciers Using the XGBoost model, we compute the surge probability of glaciers in Svalbard. The predicted probability map (Figure  6 ) indicates the presence of surge-type glaciers in the entire archipelago. This map has been computed from averaging the probability of every point along the centerline for each glacier. The map with centerline points can be found in Appendix E (Figure  E1 ). However, preferential zones of surge can be identified in for example, Nordaustlandet island, Torell Land. Other areas, for example, Nordensköld Land, Andree Land, do not gather a significant number of surge-type glaciers. The XGBoost model classifies 162 glaciers as surge-type out of 981 (see Section 2. 1 for more details on the data set). While some glacier centerlines present a uniform probability distribution, some others see their probabilities for surging evolve along the centerline (Figure  6 , inset).
Evaluation of the Surge-Type Classification Framework We calculate surging probabilities for glaciers in Svalbard after an evaluation of the best performing model, XGBoost. The discretization of the glaciers along centerlines increases the number of data points used to train and test models (981 glaciers corresponding to 97,140 points). The availability of data provides better insights into the relationships between features used as input for machine learning algorithms  (Halevy et al. , 2009). As explored in other fields (e. g. ,  Fatichi et al. (2016) ), complex natural systems cannot always be simplified using integrated features. For example, the glacier surface slope can vary along the centerline, which will change the driving stress. Averaging the slope in this case would misinform the model on changes that could impact model classification.
Fürst et al. (2018)  produced an error estimate map for the thickness computation that ranges from 1 to 500m. XGBoost is then trained with features that are not considered ground-truth (as opposed to field measurements). In addition, the error associated with the modeling data is unknown. Therefore, the range of resulting AUC values of XGBoost is controlled by possible bias in the input features used for training. The number of features is added according to their order in the feature importance. In addition, the spatial resolution of individual features differs, for example, the bed elevation has been computed on a 100m resolution whereas the runoff and CMB have been calculated on a 1 km resolution grid. The features used in the models represent as well a snapshot for a particular point in time and may therefore represent different stages in the surge-cycle.
Conclusions and Perspectives We present a framework based on machine learning models as well as a newly combined database to perform probabilistic glacier hazard mapping. The framework involves discretizing features along glacier centerlines. The most important features that explain glacier surge, that is, the width, the thickness, the runoff, the surface and bed slopes and the CMB are aligned with theories of glacier surge. Our framework allows a quantitative assessment of the surge potential of glaciers in Svalbard, that complements the previously established classification in the Randolph Glacier Inventory. Several new glaciers have been identified as surging glaciers with our model and confirmed by independent observations, which strengthens the robustness of our approach. The framework includes the comparison between different machine learning models and presents an extensive evaluation of the best model stability.
To complement theories of glacier surge, new features might be added to our framework, that is, the thickness of the underlying till, the internal reflection horizons imaging the transition between cold and temperate ice, the basal temperature and geothermal gradient, and the lithology of the underlying bed. Monitoring efforts are encouraged to be pursued toward this goal (Figure  2 ). The present study represents a snapshot in time but future studies may include multi-temporal data of certain features to enable inclusion of a time component. Finally, future work should focus on the development of an accurate method to compute a glacier-wide probability from the centerline probabilities. Our method to compute probabilistic glacier hazard mapping based on machine learning methods and a discretized database could also be applied to other regions of the world and/or adapted to other field (e.
B2. K-Fold Cross Validation Cross-validation is a common practice in machine learning to evaluate the model performance and its performance stability. We performed a ten K-Fold cross validation for XGBoost model and calculated the corresponding AUC. In Figure  B2 , we observe that the model performance is relatively stable. XGBoost is robust and the performances are not affected by the quality of the training data set. by cross-validation on the training set. The exhaustive grid search is run using the scikit-learn library of Python  (Pedregosa et al. , 2011). Table  C1  displays the three different models evaluated in our study and the hyperparameters that have been selected by the exhaustive grid search. The best values for these hyperparameter are shown in the last column.