dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis Setup We make use of the Python packages of tensorflow 2. 6. 0 and keras 2. 6. 0 to train the CNN model. Here, we use conic convolutions, meaning that the number of filters get doubled for every consecutive layer, for example 32-64-128. For embedding we just use a (learnable) lookup matrix to turn the SNP values (0,1,2) into vectors of size EmbeddingDimension = 10. We do not make use of any special embedding technique designed for NLP/words. An overview of the hyperparameters that have been used in this work are presented in Table  3. Once the CNN models are trained, these are used to evaluate the samples composing the trajectories generated by the Morris GSA method. We apply the Morris method in different configurations: • Standard Morris: Sensitivity indices are calculated for each SNP (feature), as shown in the Equations (  6 ) and (  7 ); The extremely high dimensionality of the problem would benefit from a larger sample matrix.
Results In our first experiment using the standard version of the Morris method, the values of the sensitivity measures are obtained by performing 272060 model evaluations (k = 27205 and r = 10), where each factor can vary among three levels (0, 1, 2). The ground truth of the simulated data evidences 20 SNPs (features) contributing to the final value, given in the   17 , which depict the trend of the µ * sensitivity index calculated according to the standard Morris method and Morris with groups, respectively. The index µ * allows for the detection of the most important factors without necessarily inspecting the σ index. Using the Morris method with groups, a total of k = 138 groups are defined and r = 10 trajectories are generated, resulting in a total of 1390 model evaluations. Thus, grouping factors into subgroups allows us to run a much cheaper GSA while providing results that are easily readable and consistent with the full SA for all factors.
In the following, we produce synthetic data with a realistic 3D forward model chaining the substellar and planetary atmospheric radiation and circulation global climate model (SPARC/MIT GCM) developped by  Showman et al. (2008b Showman et al. ( , 2013) )  with our 3D transmission spectrum computation module, Pytmosph3R  (Caldas et al. 2019; Falco et al. 2022). Then, we use TauREx  (Waldmann et al. 2015)  to solve the inverse problem with a 1D forward model and estimate the parameters of interest. We refer to this last part as the retrieval process or retrieval for short because we investigate whether correct parameter values are retrieved from the synthetic data. In this last part, we adopt the point of view of an observer. In Sect. 2 we explain how we built our numerical models and describe the parameterization we used.
Acknowledgements. We thank  Michiel Min  and  Quentin Changeat  for the useful discussions about the Bayesian statistical analysis. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement n •  679030/WHIPLASH ). We thank the  Programme National de Planétologie (CNRS/INSU/PNP)  and the  CNES  for their financial support. W. P. acknowledges financial support from the  Swiss National Science Foundation  for project  200021_200726.
Data from the Parkinson's Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson's Disease Biomarkers Program (PDBP) cohort. Results: 12-month PD total progression was predicted with an F-measure 0. 77, ROC AUC of 0. 77, and PR AUC of 0. 76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0. 75, ROC AUC of 0. 74, and PR AUC of 0. 73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0. 66, PR AUC of 0. 66-0. 68 for both PPMI and PDBP testing. Conclusions: Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.
They achieve good predictive 1-year accuracy (PPMI AUC 0. 75) but with reduced generalizability in the external validation set (PDBP 0. 69) except when identifying those individuals with the most severe disease  (21). More relevant from a genetics perspective, Latourelle et al. focused on prediction of longer-term (3-years or longer) progression of motor impairment (combined MDS-UPDRS II and III) trajectories using a combination of demographic, clinical, genetic, and neuroimaging features (18). They demonstrate the importance of genetic predictors, but also observe a loss in generalizability in the external validation dataset (R 2 of 27% reduced to 9% overall), with most of the separation of slow and fast progressors observed at longer (4+ years) of follow-up. Their unbiased use of genome-wide individual genetic markers, while handled with appropriate care, is prone to over tting due to population strati cation.
Methods Data. Data from the Parkinson's Progression Markers Initiative (PPMI) and Parkinson's Disease Biomarkers Program (PDBP) cohorts were accessed from the Accelerating Medicines Partnership: Parkinson's Disease (AMP-PD) repository v1 release  (25). Individuals were included in this study if they had a diagnosis of "Parkinson's Disease" or "Idiopathic Parkinson's Disease" at baseline. Data were accessed under AMP-PD Data Use Agreement and downloaded from https://amp-pd. org/. In order to maximize the real-world utility in new prospective cohorts, all standardization and correction factors that are described below are de ned in the PPMI training cohort and applied to both the PPMI training and PDBP testing cohorts. De nition of Progressors vs Non-Progressors. MDS-UPDRS subpart values were rst adjusted for medication treatment status by determining the average difference in MDS-UPDRS subpart values for treated vs untreated PPMI individuals across all timepoints and adding this average difference to the respective MDS-UPDRS subpart values of treated individuals.
Ultimately, it was determined that clustering-based approaches largely agreed with the simpler de nition, with more bias observed in the baseline ages of progressors vs non-progressors when de ned by clustering vs simple MDS-UPDRS score changes. Therefore, we proceeded with this straightforward de nition of PD progression. De nition of Predictors. We considered all available demographic, genetic, survey instrument, clinical status, clinical examination, and neuroimaging data as potential predictive features. Initially, predictive features were eliminated if they had >50% missingness. Otherwise, missing values were imputed simply using item averages for continuous predictors, median values for ordinal predictors, and the most common value for categorical predictors. After feature selection, the remaining selected features had <10% missingness prior to imputation.
Predictive features are mean-standardized using factors derived only from the training dataset and applied to the testing dataset. The Epworth Sleepiness scale was found to contribute minimal additional predictive power and in conjunction with MDS-UPDRS subpart components and excluded from use. DaTScan imagining values were only present in PPMI and thus only present in train-test split models of PPMI. Also note that whether responses were provided by the patient or caregiver are encoded in the "info source" feature in AMP-PD, but ultimately not selected as an important feature. Summary comparisons of these predictive features and their comparison in progressors vs nonprogressors are presented in Table  1  with the full set of MDS-UPDRS and MoCA elements presented in Supplemental Table  2  (PPMI) and Supplemental Table  3  (PDBP).
A further split of the PPMI training dataset into a 75% training and 25% validation data subset was performed for feature selection by SHAP values (26). To calculate SHAP values an explainer model is tuned on this internal training data subset to drive feature selection and importance. After feature selection, fully trained models are applied to the full PPMI dataset to generate nal importance plots using the sci-kit learn SHAP library. Global summary plots are then generated using the summary plot function in the SHAP library. For initial models, XGBoost models were clearly superior, achieving F1 scores of 0. 70 for MDS-UPDRS I vs. 0. 68, 0. 55, and 0. 54 for FFNN, BRF, and LR respectively, 0. 78 for MDS-UPDRS II vs 0. 63, 0. 69, and 0. 68 for FFNN, BRF, and LR respectively, and 0. 77 for MDS-UPDRS III vs 0.
70, 0. 71, 0. 69 for FFNN, BRF, and LR respectively. Similar relative results were observed for 24-and 36-month prediction. Therefore, we limited additional model development to the XGBoost predictive framework. Re ned XGBoost models were then prepared for 12-, 24-, and 36-month prediction with Optuna hyperparameter optimization (32) using the default random sampler. Only baseline features are used in these predictions, regardless of the timespan to the outcome. Final models were built by splitting PPMI into a 75% training and 25% testing set, especially to de ne the importance of neuroimaging data, otherwise nal training is performed on the full PPMI dataset and tested on the independent PDBP dataset. Strati ed random sampling was used when splitting the data to maintain the balance of progressors vs non-progressors (see Table  1 ).
Independent testing is of greatest interest, though due to the lack of DaTScan data in PDBP, DaTScan utility can only be evaluated in the PPMI train-test split models. Meta-Prediction. The output predictions of models predicting MDS-UPDRS sub-parts at 12-, 24-, and 36months are subsequently used as features for nal meta-prediction. Subpart model predictions across the different follow-up time periods are input into an XGBoost meta-predictor, in conjunction with the initial baseline features, for the nal meta-prediction of 12-month MDS-UPDRS Total progression status. Data Availability. Data was accessed and is available from AMP-PD (https://amp-pd. org/). Data was accessed under the standard AMP-PD data access agreement. Ethics Statement. Data was collected prior to this study and accessed de-identi ed.
Additionally, treatment with dopamine agents may mask PD progression, and symptomatic medications for psychiatric, sleep and autonomic symptoms may affect progression of MDS-UPDRS I scores, but granular information on these concomitant non-PD medications was not captured in the PPMI or PDBP datasets  (2). Overall, medication use is not expected to be a major determinant of progression status as evidenced by its lack of selection as a predictive feature across all prediction models. Despite these limitations, overall, we demonstrate that short-term PD progression can be well predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors in a meta-prediction framework. Meta-prediction can dissect the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression -potentially by capturing PD subtypes and their trajectories.
, 2020]  and is referenced in the French active fault database (BDFA) for seismic hazard assessment  [Jomard et al. , 2017]. However, this dextral strike-slip fault has never been observed directly at the surface by geological or morphological observations (no more than other active fault in the area). Close to Grenoble, the dextral BBF seems to be linked with old structure: the south part (and one of the south segment) of the Belledonne Middle Fault [BMF,  Thouvenot et al. , 2003 , Le Roux et al. , 2010] , but more north in Belledonne massif and close to the Maurienne swarm, the situation between actual/historical seismicity and tectonic features are still unclear. The swarm seems to be close to the Lauzière massif Fond de France Fault system (FFF), a segment which affects the south flank of the Lauzière Massif.
A set of events were not relocated because (1) occurred before the installation of the temporary YW network, (2) not attributed to a cluster (low signal-to-noise ratio, or isolated event) or (3) assigned to a cluster with a too small number of events to be relocated. The relocation with hypoDD was done in two steps. The first step allowed the relocation of a set of events consisting of one reference event per cluster, using the phases chosen in Seiscomp3. It also included some largest events not assigned to a cluster. The second step made it possible to relocate the events within each cluster: we used a crosscorrelation to find the P and S arrivals of all the events, based on the P and S phases of the Seisomp3 for a reference event. The differential times P and S between pairs of events were produced cluster by cluster, and used as input data for the hypoDD.
Focal mechanisms To complete the information produced by relocating events, focal mechanisms were calculated for large events. We used the IRIS focmec package  [Snoke et al. , 1984]  for the 41 largest events (magnitude higher or equal to 2. 4). Inputs used for the calculations were source parameters produced by hypoDD (incidence angle and azimuth), and polarities observed for the P, SH, and SV components at stations located within 50 kms from the source, including temporary and permanent stations. Figure  8  shows the focal mechanisms in map. Most of those 41 events belong to one of two families of mechanisms: N120 (strike of the rupture plane) normal mechanisms (17 events) and N60 strike-slip mechanisms (18 events). No clear pattern was observed between mechanism type and either event location within the swarm or event date.
Acknowledgments This work is part of the  SISM@LP-Swarm project  ( POIA-FEDER  Grant, funded by the  European Comission FEDER ,  Région SUD  and  Agence Nationale de la Cohésion des Territoires)  and the  URBASIS  project ( H2020-MSCA-ITN-2018 , grant number  813137 ). Authors would like to thank  LabEx OSUG@2020  (Investissements d'avenir- ANR10LABX56 ). Résif-Epos is a national research infrastructure, recognized as such by the  French Ministry of Higher Education and Research. Résif-Epos  is managed by the  Résif-Epos consortium , composed of 18 Research Institutions and Universities in France. Résif-Epos is additionally supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  (reference:  ANR-11-EQPX-0040 ) and the  French Ministry of Ecology, Sustainable Development and Energy.
because it is the point where most time has elapsed since it last depolarized. To implement this idea, I put together a Matlab program for a discretized gut (N = 200 sites), with the following rules: • At each time-step, each site can self-depolarize with a probability p • If self-depolarized, a site gives rise to 2 contractile (depolarizing) waves propagating symmetrically away from the point of depolarization, at a constant speed of 30 μm/s. • A site can only depolarize after a refractory time τ has elapsed since it last contracted. • Colliding waves annihilate, as is observed experimentally. The simulation was run over 500 time steps, and parameters p and τ were explored to reproduce the experimentally observed frequencies of contractiles waves f of 0. 6 cpm (cycles per minute) at E8  (Chevalier et al.
A first depth-age model based on four 14C ages was built in  Salvatteci et al. (2019). In this study, we added twenty-two 14 C ages and developed a new age depth-model (Table  S2 ). Radiocarbon measurements were performed on bulk sediment at the Laboratoire de Mesures du Carbone-14 (LMC14, Gif-sur Yvette, France). Ortlieb et al. (2011)  reported a regional reservoir effect (ΔR) of 511 ± 278 years for Early Holocene (10. 4-6. 8 kyr) and in the absence of ΔR data for older period we use this value to calibrated the 14C measurements for the last deglaciation as in  Salvatteci et al. (2016 Salvatteci et al. ( , 2019)). In order to construct the age model, we used the maximum probability ages obtained from the CALIB 8. 1 software. The chronological model indicates that the examined section (0-700 cm) of core M77/2-005-3 (Fig.
S2 ) recorded the LGM and the last deglaciation (22-13 kyr BP; 95-700 cm). Core M77/2-005-3 presents a hiatus at 94 cm and thus great part of the Holocene is missing. Core G14 (14ºS, 76ºW, 390 m water depth) was retrieved during the Galathea-3 expedition in 2007  (Salvatteci et al. , 2016). Radiocarbon dates of G14 were published in  Salvatteci et al. (2016). For this study, a new depth-age model was developed with an updated calibration using the CALIB 8. 1 software. The upper part of sediment layers was not recovered in G14 which ranges from 13. 4 to 24. 6 kyr BP (Fig. S2 ). The lithological description of M77/2-005-3 and G14 is available in  Salvatteci et al. (2016 Salvatteci et al. ( , 2019)). M77/2-005-3 and G14 cores show laminated and banded sediments with no evident signs of major discontinuities during the last deglaciation  (Salvatteci et al.
, 2016 (Salvatteci et al. , , 2019)). G14 core presents more continuous laminated packaged in comparison to M722-005-3 core. (Salvatteci et al. , 2016 (Salvatteci et al. , , 2019)). In order to compare our new data from cores M77/2-005-3 and G14 with previously published records in the area we modified the age model of core 106 KL  (Rein et al. , 2005). Core 106 KL (12°030S, 77°39. 80W, 184 m water depth) was retrieved during cruise SONNE 147  (Rein et al. , 2004 (Rein et al. , , 2005)). Chronology model and lithology were fully described in  Rein et al. (2004 Rein et al. ( , 2005)). A new depth-age model was developed with an updated calibration using the CALIB 8. 1 software (Fig. S2 ) and we only used the sections covering the last deglaciation.
Acknowledgements This publication was made possible through support provided by the  IRD-DPF  and the  MAGNET Program  of  CONCYTEC Nº007-2017. This work was supported by the  International Joint Laboratory "PALEOTRACES" (IRD, France ;  UPMC, France ;  UFF, Brazil ;  UA, Chile ;  UPCH, Peru)  and  ANR-15-JCLI-0003-03   BELMONT FORUM PACMEDY. Grain-size analyses were performed on the  ALYSES facility (IRD-Sorbonne University, Bondy, France ), which is supported by grants from  Région Ile-de-France. This work is a contribution of the  Collaborative Research Project 754 "Climate-Biogeochemistry interactions in the Tropical Ocean " (www. sfb754. de), which is supported by the  Deutsche Forschungsgemeinschaft (DFG). We would like to thank the crew and scientists aboard R/V Meteor cruises M77/2 in 2008.
Main conclusion: Expert maps of species' global distributions are limited in their spatial detail and temporal specificity, but form a useful basis for broad-scale characterizations and model-based integration with other data. We provide georeferenced range maps for the native ranges of all extant mammal species as shapefiles, with species-level metadata and source information packaged together in geodatabase format. Across the three taxonomic sources our maps entail, there are 1784 taxonomic name differences compared to the maps currently available on the IUCN Red List website. The expert maps provided here are harmonised to the MDD taxonomic authority and linked to a community of online tools that will enable transparent future updates and version control.
For example, speciation history and physiographic barriers may provide information on the broad regions that a species occupies, and information on habitat preferences, published or unpublished occurrence records and local knowledge, may be used to delimit the range within that area. The extent to which range maps demarcate populations into separate polygons can also differ. An array of data sources and types may therefore be used to generate any given range map, and the combinations will vary across species and even between different regions of a given species' range. Range maps are particularly important where observation-or specimen-based occurrence records are sparse, such as in the tropics, where they may represent the only distributional knowledge of a species. Over recent decades, expert range maps have gained further recognition as quantitative spatial objects that can be aggregated to map biogeographic patterns, including taxonomic richness, range size and population genetics  (Fritz et al.
They can also quickly become out-of-date, often due to taxonomic revisions (e. g. , species splits or merges) or range boundary changes (e. g. , range shifts, or the discovery or extinction of subpopulations). Although separate polygons for introduced range components are sometimes included, range maps typically exclude introductions outside of the species' native ranges. Nevertheless, range maps continue to serve an important role, especially in combination with ancillary data such as species habitat preferences and land cover information to further refine species ranges and as one of several inputs into a new generation of modelbased species distribution predictions  (Merow et al. , 2017; Pacifici et al. , 2018; Powers & Jetz, 2019; Visconti et al. , 2011). Here, we provide fully georeferenced digital range maps for the native ranges of all extant species of mammals, globally harmonised to three taxonomies: the authoritative mammal taxonomy provided in the Mammal Diversity Database (MDD v1.
2;  Burgin et al. , 2018) , the Handbook of the Mammals of the World (HMW), which was published across nine volumes  (Mittermeier et al. , 2013; Wilson et al. , 2016 Wilson et al. , , 2017;; Wilson & Mittermeier, 2009 , 2011 , 2014 , 2015 , 2018 , 2019) , and the Illustrated Checklist of the Mammals of the World (CMW;  Burgin et al. , 2020a Burgin et al. , , 2020b)). Each of the three datasets are therefore represented by range maps for all species in each taxonomy, tracking any lumps or splits that occurred between them, as well as any name changes, updates to the ranges and newly described species. The maps are based primarily on the HMW and CMW, and, for the MDD, supplemented with spatial information from other primary source materials, where taxonomic revisions have occurred since the publication of the HMW and CMW compendia.
The final range maps are not exact replicas of those published in any one source, as changes are introduced during the interpretation and georeferencing stages, as well as other processing steps, such as clipping to coastlines for terrestrial species. The three taxonomic authorities represent different temporal snapshots of mammal taxonomy. The nine volumes of the HMW were published over the course of a decade. The CMW aimed to unify the HMW taxonomy into a single two-volume publication while providing updates to taxonomy and geographic ranges. The MDD was first published in 2018 as a joint effort of the American Society of Mammalogists and the National Science Foundationsupported VertLife Terrestrial project (vertl ife. org) to centralise all published taxonomic changes to mammals as a freely available online database (mamma ldive rsity.
org). The most recent version of the MDD builds upon the CMW and HMW compendia, but provides further updates to taxonomy, synonyms and primary reference information using a wide range of primary literature. The biggest change from the CMW to the MDD is the decision to revert the taxonomy of Bovidae, which follows  Groves and Grubb (2011)  in the former, to the older taxonomic arrangement presented by Grubb  (Grubb, 2005a (Grubb, , 2005b) )  for the MDD v1. 2 (see https://zenodo. org/ recor d/4139818). The rest of the Perissodactyla and non-cetacean Artiodactyla also follow this older arrangement with some modifications (e. g. , Giraffa;  Petzold & Hassanin, 2020). The older arrangement is viewed as more stable and justifiable in the mammalogical community  (Gutiérrez & Garbino, 2018; Holbrook, 2013) , and results in a reduction of 124 extant species in the MDD taxonomy that had been split in the CMW.