dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatial predictive analysis In order to evaluate the performance of the machine learning SMB models in space, we perform a LOGO (leave-oneglacier-out) cross validation. For relatively small datasets like the one used in this study, cross validation ensures that the model is validated on the full dataset. Such validation aims at understanding the model's performance for predictions on other glaciers for the same time period as during the training. An important aspect is the comparison between the linear and nonlinear machine learning algorithms used in this study. Steiner et al. (2005)  already proved that a nonlinear ANN improved the results with respect a classic stepwise multiple linear regression. Here, we draw a similar comparison using more advanced methods for a larger dataset: OLS and Lasso as linear machine learning algorithms and a deep ANN as a nonlinear one.
The ANN solves this problem, with an increased explained variance which translates into a better accuracy for extreme SMB values, even without the use of sample weights (Fig. 6 ). As a consequence, the added value of deep learning is especially relevant on glaciers with steeper annual changes in glacier-wide SMB (Fig. 7a ). The use of sample weights can scale this factor up or down, thus playing with a performance trade-off depending on how much one wants to improve the model's behaviour for extreme SMB values. Overall, deep learning results in a lower error throughout all the glaciers in the dataset when evaluated using LOGO cross validation (Fig. 8 ). Moreover, the bias is also systematically reduced, but it is strongly correlated to the one from Lasso.
9 , we found remarkable improvements between the linear Lasso and the nonlinear deep learning in both the explained variance (between +94 % and +108 %) and accuracy (between +32 % and +58 %). This implies that 35 % more variance is explained using a nonlinear model in the temporal dimension for glacier-wide SMB balance in this region. Deep learning manages to keep very similar performances between the spatial and temporal dimensions, whereas the linear methods see their performance affected most likely due to the increased nonlinearity of the SMB reaction to meteorological conditions. A more detailed year-by-year analysis reveals interesting information about the glacier-wide SMB data structure. As seen in Fig. 10 , the years with the worst deep learning precision are  1984, 1985 and 1990.
All three hydrological years present high spatial variability in observed (or remotely sensed) SMBs: very positive SMB values in general for 1984 and 1985, with few slightly negative values, and extremely negative SMB values in general for 1990, with few almost neutral values. These complex configurations are clearly outliers within the dataset which push the limits of the nonlinear patterns found by the ANN. The situation becomes even more evident with Lasso, which struggles to resolve these complex patterns and often performs poorly where the ANN succeeds  (e. g. years 1996, 2012 or 2014). The important bias present only with Lasso is representative of its lack of complexity towards nonlinear structures, which results in an underfitting of the data. The average error is not bad, but it shows a high negative bias for the first half of the period, which mostly has slightly negative glacier-wide SMBs, and a high positive bias for the second half of the period, which mostly has very negative glacier-wide SMB values.
5 km 2 ), a standardized flat function is used in order to make them shrink equally at all altitudes. This is done to simulate the fact that generally, the equilibrium line of very small glaciers surpasses the glacier's maximum altitude, thus shrinking from all directions and altitudes in summer. Moreover, due to their reduced size and altitudinal range, the ice flow no longer has the same importance as for larger-or medium-sized glaciers. In order to evaluate the performance of the parameterized glacier dynamics of ALPGM, coupled with the glacier-wide SMB component, we compared the simulated glacier area of the 32 studied glaciers with the observed area in 2015 from the most up-to-date glacier inventory in the French Alps. Simulations were started in 2003, for which we used the F19 ice thickness dataset.
In order to take into account the ice thickness uncertainties, we ran three simulations with different versions of the initial ice thickness: the original data and -30 % and +30 % of the original ice thickness, in agreement with the uncertainty estimated by the authors. Moreover, in order to take into account the uncertainties in the h glacier geometry update function computation, we added a ±10 % variation in the parameterized functions (Fig. 11 ). Overall, the results illustrated in Fig. 11  show good agreement with the observations. Even for a 12-year period, the initial ice thickness still has the largest uncertainty, with almost all glaciers falling within the observed area when taking it into account. The mean error in simulated surface area was 10. 7 % with the original F19 ice thickness dataset.
Training deep learning models with spatiotemporal data The creation and training of a deep ANN requires a certain knowledge and strategy with respect to the data and study focus. When working with spatiotemporal data, the separation between training and validation becomes tricky. The spatial and temporal dimensions in the dataset cannot be ignored and strongly affect the independence between training and validation data  (Roberts et al. , 2017; Oliveira et al. , 2019). Depending on how the cross validation is performed, the obtained performance will be indicative of one of these two di- mensions. As is shown in Sect. 3. 2. 3, the ANNs and especially the linear modelling approaches had more success in predicting SMB values in space than in time. This is mostly due to the fact that the glacier-wide SMB signal has a greater variability and nonlinearities in time than in space, with climate being the main driver of the annual fluctuations in SMB, whereas geography, and in particular the local topography, modulates the signal between glaciers  (Huss, 2012; Rabatel et al.
By balancing all these different effects, one can achieve the ratio of accuracy to generalization that best suits a certain dataset and model in terms of performance. Nonetheless, one key aspect in machine learning models is data: expanding the training dataset in the future will allow us to increase the complexity of the model and its performance. Consequently, machine learning models see their performance improved as time goes by, with new data becoming available for training. Although the features used as input for the model are classical descriptors of the topographical and meteorological conditions of the glaciers, it is worth mentioning that applying the model in different areas or with different data sources would likely require a re-training of the model due to possible biases: different regions on the globe may have other descriptors of importance, but also different measuring techniques will likely have different biases.
g. Réveillet et al. , 2017) , it could be an interesting way to expand the training dataset for glaciers in tropical and subtropical regions, where shortwave radiation plays a much more important role  (Benn and Evans, 2014). Maussion et al. (2015)  followed a similar approach with linear machine learning in order to calibrate a regression-based downscaling model that linked local SEB-SMB fluxes to atmospheric reanalysis variables. In this work, we also evaluated the resilience of the deep learning approach: since many glacierized regions in the world do not have the same number of data used in this study, we trained an ANN only with monthly average temperature and snowfall, without any topographical predictors, to see until which point the algorithm is capable of learning from minimal data.
The results were quite interesting, with a coefficient of determination of 0. 68 (against 0. 76 from the full model) and a RMSE of 0. 59 m w. e. a -1 (against 0. 51 from the full model). These results indicate that meteorological data are the primary source of information, determining the interannual high-frequency variability in the glacierwide SMB signal. On the other hand, the "bonus" of topographical data helps to modulate the high-frequency climate signal by adding a low-frequency component to better differentiate glaciers and the topographical characteristics included in the glacier-wide SMB data  (Huss et al. , 2012). The fact that glacier-wide SMB is influenced by glacier topography poses the question of determining if the simulated glacier geometries can correctly reproduce topographical observations, needed to represent the topographical feedback present in glacier-wide SMB signals.
The comparison with other SMB models is beyond the scope of this study, but it would be worth investigating to quantify the specific gains that could be achieved by switching to a deep learning modelling approach. Nonetheless, the linear machine learning models trained with the CPDD and cumulative snowfall used in this study behave in a similar way to a calibrated temperature-index model. Even so, we believe that future efforts should be taken towards physics-informed datascience glacier SMB and evolution modelling. Adding physical constraints in ANNs, with the use of physics-based loss functions and/or architectures (e. g. Karpatne et al. , 2018) , would allow us to improve our understanding and confidence in predictions, to reduce our dependency on big datasets, and to start bridging the gap between data science and physical methods  (Karpatne et al.
, 2017; de Bezenac et al. , 2018; Lguensat et al. , 2019; Rackauckas et al. , 2020). Deep learning can be of special interest once applied in the reconstruction of SMB time series. More and more SMB data are becoming available thanks to the advances in remote sensing (e. g. Brun et al. , 2017; Zemp et al. , 2019; Dussaillant et al. , 2019) , but these datasets often cover limited areas and the most recent time period in the studied regions. An interesting way of expanding a dataset would be to use a deep learning approach to fill the data gaps based on the relationships found in a subset of glaciers as in the case study presented here. Past SMB time series of vast glacierized regions could thereby be reconstructed, with potential applications in remote glacierized regions such as the Andes or High Mountain Asia.
Taking into account that this nonlinearity substantially improved the explained variance and accuracy compared to linear statistical models, especially in the more complex temporal dimension. As we have shown in our case study, deep ANNs are capable of dealing with relatively small datasets, and they present a wide range of configurations to generalize and prevent overfitting. Machine learning models benefit from the increasing number of available data, which makes their performance constantly improve as time goes by. Deep learning should be seen as an opportunity by the glaciology community. Its good performance for SMB modelling in both the spatial and temporal dimensions shows how relevant it can be for a broad range of applications. Combined with in situ or remote-sensing SMB estimations, it can serve to reconstruct SMB time series for regions or glaciers with already-available data for past and future periods, with potential applications in remote regions such as the Andes or the High Mountain Asia.
Moreover, deep learning can be used as an alternative to classical SMB models, as is done in ALPGM: important nonlinearities from the glacier and climate systems are potentially ignored by these mostly linear models, which could give an advantage deep learning models in regional studies. It might still be too early for the development of such models in certain regions which lack consistent datasets with good spatial and temporal coverage. Nevertheless, upcoming methods adding physical knowledge to constrain neural networks (e. g. Karpatne et al. , 2018; Rackauckas et al. , 2020)  could provide interesting solutions to the limitations of our current method. By incorporating prior physical knowledge into neural networks, the dependency on big datasets would be reduced, and it would allow transitioning towards more interpretable physics-informed data-science models.
Code and data availability. The source code of ALPGM is available at https://github. com/JordiBolibar/ ALPGM (last access: February 2020) with its DOI (https://doi. org/10. 5281/zenodo. 3609136, Bolibar, 2020) for the v1. 1 release. All scripts used to generate plots and results are included in the repository. The detailed information regarding glaciers used in the case study and the SMB model performance per glacier are included in the Supplement. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-14-565-2020-supplement. Author contributions. JB developed ALPGM, analysed the results and wrote the paper. AR conducted the glaciological analysis, contributed with the remote-sensing SMB data and conceived the study, together with IG, TC and JB.
CG and JB developed the deep and machine learning modelling approach, and ES contributed in the statistical analysis. All authors discussed the results and helped with developing the paper. Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. This study has been possible through funding from the ANR VIP_Mont-Blanc (ANR-14 CE03-0006-03), the BERGER project (Pack Ambition Recherche funded by the Région Auvergne-Rhône-Alpes, the LabEx OSUG@2020 (Investissements d'avenir -ANR10 LABX56), and the CNES via the KALEIDOS-Alpes and ISIS (Initiative for Space Innovative Standard) projects. Data have been provided by Météo-France (SAFRAN dataset) and the GLACIOCLIM National Observation Service (in situ glaciological data). The authors would like to thank Eduardo Pérez-Pellitero (Max Planck Institute for Intelligent Systems) for the interesting discussions regarding deep learning, Benjamin Renard (Irstea Lyon) for his insightful comments on statistics, and Fabien Maussion and an anonymous reviewer for their constructive review comments which have helped to improve the overall quality and clarity of the paper.
Financial support. This research has been supported by the ANR VIP_Mont-Blanc (grant no. ANR-14 CE03-0006-03), the Pack Ambition Recherche funded by the AuRA region (BERGER project grant), the LabEx OSUG@2020 (grant no. ANR10 LABX56) and the CNES (KALEIDOS-Alpes and ISIS projects grants). Review statement. This paper was edited by Valentina Radic and reviewed by Fabien Maussion and one anonymous referee.
| Protein structures and variant databases The following experimental structures were downloaded from the Protein Data Bank (PDB)  (Berman et al. , 2000)  and used in our study: CYP 1A2 (PDB ID: 2HI4)  (Sansen et al. , 2007) , CYP 2B6 (PDB ID: 3IBD)  (Shah et al. , 2010) , CYP 2C9 (PDB ID: 1OG2)  (Williams et al. , 2003) , complement factor B (PDB ID: 2OK5)  (Milder et al. , 2007) , coagulation factor VIII (PDB ID: 2R7E)  (Shen et al. , 2008) , and antithrombin (PDB ID: 2BEH)  (Johnson et al. , 2006). Heteroatoms were removed except for the heme group of the CYP proteins. Earlier, the nomenclature and properties of allelic variants of human CYP could be found at http://www. cypal leles. ki. se  (Sim & Ingelman-Sundberg, 2013). However, the Human Cytochrome P450 Allele Nomenclature Database can now be found at the Pharmacogene Variation (PharmVar) Consortium  (Gaedigk et al.
, 2018). Thus, all investigated amino acid changes for the three types of CYP proteins were taken from the PharmVar updated list. Some missing side chains or engineered residues present in the experimental structures were added or changed back to the wild-type sequence with the Chimera software  (Pettersen et al. , 2004). The PyMOL Molecular Graphics System (Version 1. 8. 2. 2 Schrödinger, LLC) and Chimera were both used for the structural analysis. 2. 2 | Computational approaches and databases 2. 2. 1 | PolyPhen-2 We selected PolyPhen-2 (Polymorphism Phenotyping)  (Adzhubei et al. , 2010)  to investigate the amino acid changes because this method is fast, user-friendly and used in many laboratories. The tool is mainly sequence based but it also incorporates some structural information when available to produces qualitative predictions.
| Protein stability prediction in 3D For the assessment of the protein stability, we used four different programs: PopMusic, DUET, MaestroWeb, and SAAFEC. These web servers compute ΔΔG values between the wild-type and the variant proteins. PoPMuSiC  (Gonnelli, Rooman, & Dehouck, 2012 ) is a software which evaluates the changes in the stability of a given protein upon amino acid changes. The server predicts the thermodynamic stability changes caused by a single site substitution using a linear combination of statistical potentials whose coefficients depend on the solvent accessibility of the modified residue  (Dehouck, Kwasigroch, Gilis, & Rooman, 2011). DUET is a software that evaluates protein stability with an optimized predictor that makes use of support vector machine approaches  (Pires, Ascher, & Blundell, 2014aa).
Based on the MM-PBSA method  (Homeyer & Gohlke, 2012)  with weight coefficients, this approach was optimized using experimental data from the ProTherm database  (Bava, Gromiha, Uedaira, Kitajima, & Sarai, 2004). With this approach, the protein structures undergo an energy minimization using the NAMD software  (Phillips et al. , 2005).
| Automatic 3D online structural mapping of missense variants Orengo, & Thornton, 2020). The Missense3D pipeline maps and analyze amino acid changes on experimental and homology model protein 3D structures. VarSite maps known disease-associated variants from UniProt (UniProt Consortium, 2019), ClinVar  (Landrum et al. , 2014) , and gnomAD (genome aggregation database)  (Karczewski et al. , 2019)  or data provided by the users onto protein experimental 3D structures. A disease propensity score is also reported, the value quantifies how much more often a variant is observed in diseases than in the natural variant data obtained from gnomAD. The value ranges from very low (propensity = 0. 25) to very high (propensity = 3. 27). On both servers, users obtain a report card with information about the amino acid substitution.
| Protein flexibility Some regions of proteins can be moderately to highly flexible. Flexibility can be inferred in some cases from X-ray experiments, obtained from NMR studies or explored using long molecular simulation approaches. Yet, some very fast approaches have been reported to provide relatively accurate predictions without a need for CPU/GPU intensive calculations. We here employed the predicted B-factor (relative vibrational motion) and RMSFs (root-mean-square fluctuations) obtained from the prediction program PredyFlexy (fast computations carried out only over the protein sequences)  (de Brevern, Bornot, Craveur, Etchebest, & Gelly, 2012). Three types of flexibility are proposed by this approach. PredyFlexy classifies amino acid residues into rigid, intermediate or flexible sites.
CYP proteins For these investigations we used the ChannelsDB database  (Pravda et al. , 2018). Channels, tunnels, and pores are very important structural features within biomacromolecules. Tunnels connect internal spaces with exterior regions. Enzyme active sites can be connected to the exterior environment by one or more channels passing through the protein  (Louet et al. , 2018; Pravda et al. , 2014). Here, CYP proteins were investigated with such tools.
| CYP1A2 For CYP1A2 (PDB ID: 2HI4, UniProt: P05177), we investigated the possible molecular effects of two clinically important alterations (Asp348Asn and Ile386Phe) (Table  2 ). Human cytochrome P450 1A2 catalyzes important reactions during the metabolism of xenobiotics, including N-hydroxylation of carcinogenic aromatic amines. Variants Asp348Asn and Ile386Phe were expressed at levels less than half of the wild-type proteins and these differences (vs. wildtype) were statistically significant (p < 0. 05)  (Zhou, Josephy, Kim, & Guengerich, 2004). Table  2  shows results of the in silico analyses for CYP1A2 (PDB ID: 2HI4). CYP1A2 is not processed yet on the ChannelsDB server. Yet, from our previous work, it seems possible that Ile386 is located in one of the substrate-binding channel (see Figure  6 , channel 2f in  Louet et al.
, 2018) , and it is not predicted to be involved in a protein-protein interaction site (Table  2 ). Overall, three of the four 3D web servers suggest that this substitution could be problematic, in overall agreement with our 3D interactive structural analysis suggesting a possible destabilizing effect for this substitution. This in turn is in agreement with the in vitro experimental data since it was found that the expression level of the mutant was lower than that of the wild type, while the activity level of the mutant was also decreased  (Zhou et al. , 2004). Missense3D, reports that no structural damage is detected for this substitution (i. e. , the salt-bridge, about 3. 6 Å between the charged groups, is not found) while VarSite underlines that the residue is highly conserved (in 110 aligned sequences), that the protein function could be perturbed and that there are not natural variants recorded in the gnomAD database at this position.
| CYP2B6 For CYP2B6 (PDB ID: 3IBD, UniProt: P20813), we investigated the possible molecular effect of four clinically important alterations (Table  3 ). The drugs metabolized by CYP2B6 include the prodrug cyclophosphamide, the antimalarial artemisinin, the anesthetics ketamine and propofol, and the HIV-1 reverse transcriptase inhibitors nevirapine and efavirenz  (Lang et al. , 2004). In vitro studies revealed that these CYP2B6 protein variants (Arg140Gln, Gly99Glu, and Ile391Asn) have a reduced expression level and/or activity. Two amino acid changes (Gly99Glu and Ile391Asn) resulted in almost undetectable enzyme activity, despite the presence of residual protein level  (Lang et al. , 2004). Resequencing defined loss-of-function allele *27 (Met198Thr), resulted in 85% decrease in enzyme activity  (Rotger et al.
| CYP2C9 CYP2C9 metabolizes approximately 15% of the clinically used drugs  (Hirota, Eguchi, & Ieiri, 2013; Yiannakopoulou, 2013) , including hypoglycemic agents, anticonvulsants, anticoagulants (e. g. , warfarin), nonsteroidal anti-inflammatory (e. g. , diclofenac), antihypertensive (e. g. , losartan), and diuretic drugs  (Isvoran et al. , 2017). We selected four clinically important alterations (Arg150His, Ile359Leu, Ile434Phe, and Arg132Gln) associated with altered drug metabolism by >50%. Table  4  shows the results of the in silico analysis for CYP2C9 (PDB ID: 1OG2, UniProt: P11712). Six channels were found for the CYP2C9 protein (PDB ID: 1OG2) with ChannelsDB  (Pravda et al. , 2018). None of the studied amino acids is involved in these channels. With regard to the predicted ligand-binding pockets, 12 pockets were found for CYP2C9 using the FTMap approach  (Brenke et al.
| Complement Factor B For Complement FB (PDB ID: 2OK5, UniProt ID: P00751), we investigated the possible molecular effect of four clinically important alterations involved in strengthening and facilitating the formation of an overactive C3 convertase (Table  5 ). The mutations were identified in patients with aHUS  (Marinozzi et al. , 2014; Roumenina et al. , 2009). For this protein, we predicted putative ligand-binding pockets with the FTMap. Eleven pockets were identified (Table  S4 ). None of the four amino acids investigated here were found to be in a predicted ligand-binding pockets.