dataesr/scientific-paragraphs-categorization huggingface dataset:
0 dataset  (Knox et al. 2019; Delwiche et al. 2021). Beyond the baseline predictors of  Knox et al. (2019) , the use of all predictors at each site was also tested, providing a large and comparable predictor set that always included soil temperature, and soil moisture, and/or water table position, among others (Table  3 ). Although availability of these additional predictors varied widely across other FLUXNET-CH4 sites, for these 17 sites, the additional predictors constituting the all-predictor set were highly consistent. Missing predictor data were mean-imputed and "imputed flag" predictors were created, which is standard in ML.
Gap-filling Methods Marginal Distribution Sampling and four ML algorithms were used for gap-filling, including lasso regression, artificial neural networks, random forests, and gradient boosted decision trees. Each ML algorithm was trained using the four different predictor subsets at each site. The "xgboost" package (Chen and Guestrin 2016) was used to implement the gradient boosted decision tree models and the "scikit-learn" package  (Pedregosa et al. 2011)  in python (Van Rossum and Drake 2009) was.
2011; Schuurmans 2006 ). Performance metric comparisons were implemented in R (R Core Team 2021) using the PMCMR package  (Pohlert 2014). To evaluate whether the gap-filling performance is related to the characteristics of CH 4 flux, Pearson correlation coefficient between the best model performance metrics (RF and all predictors) and the annual mean and variance of the fluxes were analyzed. Correlation analyses were performed in Python using the 'scipy' package  (Virtanen et al. 2020).
Evaluating Systematic USTAR Bias Filtering to remove eddy covariance CH 4 fluxes during low turbulence conditions (using friction velocity, USTAR, as a measure of turbulence) may introduce a systematic bias into ML training because the efficiency of CH 4 gas transport mechanisms such as plant mediated flow can increase with wind speed  (Laanbroek 2010 ). To approximate an evaluation of biases introduced from low USTAR filtering, the amount of filtered data across each site was quantified (0-21%) and the same fraction of high USTAR conditions (top percentile) was removed from each paired training and validation set. The original and high-USTAR-filtered model performance was then evaluated on the scorable gaps created with the high USTAR filter. Although an imperfect analogue, this test therefore simulated model extrapolation to very low USTAR conditions by evaluating performance during extrapolations to high USTAR conditions.
g. , multiple months) is much less reliable, regardless of the method used, and are best avoided entirely, if possible. Decision tree-based models (RF and XGBoost) showed better performance than ANN and Lasso models across the majority of the 17 wetland and rice paddy sites. This is consistent with recent work on CH 4 gap-filling which demonstrated that a RF gap-filling model outperformed both ANN and support vector regression models across five wetland and rice paddy sites  (Nemitz et al. 2018; Kim et al. 2020; Knox et al. 2019). RF models are also relatively easy to tune, fast to train even on large datasets, and require little preprocessing. Furthermore, decision-tree-based models are more interpretable (presently) than ANN  (Russell and Norvig 1995) , which enables analysis of important predictors.
In comparison to ML approaches, MDS was tested as an easy and fast method that makes use of only three predictors. MDS scored highly on average although still much lower than the best ML models. Kim et al. (2020)  also found that MDS more frequently introduced statistical bias in annual sums than ML models. Although RF and ANN models are recommended ML methods, there is still room to improve their gap-filling performance, especially on long gaps. Recent deep neural network architectures have shown impressive results in modeling long sequences in natural language processing, particularly recurrent neural network variants  (Lipton et al. 2015)  and Transformers  (Vaswani et al. 2017). These models have the potential to reproduce highly nonlinear variable interactions using large datasets including half-hourly time series flux data and may be able to capture lagged relationships between predictors and CH 4 flux without further manual revision.
Integrated Emissions & Uncertainties Computing annual or growing season CH 4 emissions requires gapfilling because filtering of EC data and other acquisition issues typically creates gaps of a wide variety of lengths, and especially an abundance of short gaps (Table  C2 ). Gaps are not normally distributed in time and therefore FCH4 observations are likely to be biased, which will propagate to the time-integrated flux. However, the investigator must decide: 1) which gap-filled values are likely to be of sufficient accuracy to be retained, and 2) whether the retained gap-filled plus observed values are sufficient to integrate emissions over an annual, seasonal, or other timeframe. As a rough guide, filled values should be treated with greater scrutiny as they become longer and less frequent in the scorable dataset.
The most abundant scorable gaps of length one half-hour to approximately 12 days can be filled confidently, given performance metric checks as described in this study. Investigators should, however, be aware that episodic fluxes, perhaps due to ebullition events, may not always be captured and instead may be filled with average fluxes for the most comparable conditions (e. g. , FCH4 and MAE spikes in Figure E. 2). Greater scrutiny of evaluation metrics is recommended for gaps longer than approximately 12 days, but less than multiple months, whereas, filled values in gaps of multiple months (> 60 days) should generally be excluded, as is done in CO 2 gap-filling  (Wutzler et al. 2018). The exception may be very long (decadal) datasets where the monthly-scale gap occurs in a season with ample data from other sites-years and can be reasonably evaluated.
Furthermore, probabilistic models like Gaussian processes or multiple imputation methods may be able to produce well-calibrated models without the need for post-processing calibration procedures  (Vitale et al. 2018; Camps-Valls et al. 2019). Recently, a method for producing uncertainty estimates from any gradient boosting model was introduced which may enable decision tree models to produce well-calibrated, probabilistic predictions without requiring a model ensemble or post-processing calibration  (Duan et al. 2020). Finally, deep learning models can capture highly nonlinear relationships in large datasets and make probabilistic predictions which have the potential to outperform other gap-filling methods.
ML prediction uncertainties, in contrast, can be calibrated to observations using Platt scaling. Finally, based on variable importance results, it is recommended that soil temperature and water table depth are measured at all wetland eddy covariance sites. The python code for developing gap-filling methods, comparing predictions, and calibrating uncertainties is available [https://github. com/stanfordmlgroup/ methane-gapfill-ml]. For future evaluations at wetlands and other ecosystems, this code can provide a foundation for the development of standardized eddy covariance CH 4 processing by different teams and Regional Flux Networks which can also be tested on nitrous oxide fluxes as longer time series become available  (Papale 2020). the project. EFC, FL, GM, JI, SZ, VL, and ZO conceived of, and AA, ALM, CT, DP, DV, and IM contributed to, the design and execution of the machine learning analysis.
ALM was consulted with on machine learning model and artificial gap evaluation. CT and DP contributed the marginal distribution sampling analysis. DV was consulted with on multi-imputation methods. EFC, FL, GM, JI, SZ VL, and ZO wrote the initial draft of the manuscript and ALM, ACRS, ACV, ADR, AK, AL, AM, AN, ARD, BM, BRKR, CH, CS, CT, DDB, DIC, DP, DV, DYFL, DZ, EE, EJW, ESH, GB, GJ, GLV, GXW, HC, HI, HJD, IM, JC, KBD, KSH, KVRS, LM, LWM, MA, MBN, MG, MHelbig, MHeimann, MP, MU, OS, PA, RBJ, RV, SB, SF, SHK, and TH contributed edits to subsequent drafts. ADR, ARD, CH, DDB, DIC, DYFL, GB, GLV, HI, HJD, IM, JC, KVRS, MA, MBN, MH, MU, OS, TF, TH, and TS were affiliated as principal investigators for the 17 core analysis sites. All other coauthors contributed data as principal investigators or were named as affiliated team members at other FLUXNET-CH4 sites.
Acknowledgments This study was supported by the  Gordon and Betty Moore Foundation  through Grant  GBMF5439  " Advancing Understanding of the Global Methane Cycle " to  Stanford University  supporting the Methane Budget activity for the Global Carbon Project (globalcarbonproject. org). BRKR was supported by  NSF  Award  1752083. OS was supported through the  Canada Research Chairs and Natural Sciences and Engineering Research Council Discovery Grants programs. TS and CW were supported by the  Helmholtz Association of German Research Centres  (Grant No. VH-NG-821 ). GB was supported by a  US Department of Energy  (Grant  DE-SC0021067 ) and  NOAA   Davidson Fellowship Award  administered by  ODNR OWC-NERR. Funding from the  SNF projects DiRad and InnoFarm  ( 146373  and  407340_172433 ), from the  ETH Board  and from  ETH Zurich  is greatly acknowledged.
DP, CT and DV were supported by the  Department of Excellence 2018 Program MIUR Project  " Landscape 4. 0 -food, wellbeing and environment" and the  ICOS Ecosystem Thematic Centre. CT was supported by the  E-SHAPE  ( GA820852 )  H2020   European  project. DB, JV, DS, CS, SK, EE, KSH, KK, AV, CRS, RS (or sites  US-MYB ,  US-TW1 ,  US-TW3 ,  US-TW4 ,  US-TW5 ,  US-TWT, US-SND, US-SNE)  were supported by the  California Department of Water Resources  through a contract from the  California Department of Fish and Wildlife  and the  United States Department of Agriculture (NIFA  grant # 2011-67003-30371 ). Funding for the AmeriFlux core sites was provided by the  U. S. Department of Energy's Office of Science  (AmeriFlux contract # 7079856 ). KK was supported by the  Estonian Research Council  grants No.
PSG631  and  PRG352. KSH was supported by the  California Sea Grant Delta Science Fellowship (programs R/SF-70 , grant no. 2271 ). The contents of this material do not necessarily reflect the views and policies of the  Delta Stewardship Council or California Sea Grant , nor does mention of trade names or commercial products constitute endorsement or recommendation for use. MU was supported by the  Arctic Challenge for Sustainability II  ( JPMXD1420318865 ) and the  JSPS KAKENHI  ( 20K21849 ). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government. IM thanks  H2020   RINGO  project (Grant Agreement  730944 ), the  Academy of Finland Flagship funding  (grant no. 337549 ) and  ICOS-Finland by University of Helsinki  funding.
We also investigated three rock glaciers not presenting destabilisation (hereafter referred to as 'non-destabilised') to obtain displacement evolution rates that could be compared to the destabilised landforms. In this local-scale analysis, we used a higher number of orthoimages, specifically computed from aerial (IGN) and Unmanned Aerial Vehicle (UAV) photogrammetry. To generate the orthoimages for each site, we used the Structure from Motion approach  42  , here integrated into Agisoft Photoscan software. We used aerial imagery acquired by the IGN since the 40s at a plurennial frequency (generally, five to fifteen years) and recent UAV surveys conducted by our team. We also used the orthoimages to compute the surface velocity and strain rate fields, and explore the possible mechanical drivers of crevasses at the local sites, as has been done in glaciological studies  43.
For the destabilised rock glaciers, we calculated the strain rates between non-destabilised and destabilised areas. For non-destabilised rock glaciers, we evaluated the strain rates between the available upper and lower moving targets. In Step 3, to explore the hypothesis of a statistically significant correlation between climate and destabilisation, we conducted an analysis of the link between this phenomenon and climate changes. In particular, we evaluated the correlation significance between the dynamic evolution of both destabilised and nondestabilised rock glaciers and the evolution of air temperature anomalies-extracted for each rock glacier site using the SAFRAN (Système d'Analyse Fournissant des Renseignements Atmosphériques à la Neige) meteorological reanalysis  44  and HISTALP (Historical Instrumental climatological Surface Time series of the greater Alpine region) database  45.
The fastestmoving boulder is used in the statistical analysis. The suitability of the one-boulder approach used in the regional-scale analysis was assessed by comparing the data obtained with the displacement rates evaluated at the local sites on multiple boulders. On a sample of 48 displacement rates, we observed good agreement between the displacement rates evaluated from regionaland local-scale analysis (R 2 = 0. 96, median error = 0. 19 my -1 ), suggesting that the one-boulder approach is a valid first-order approximation of rock glacier maximum displacement rates. Uncertainty assessment. Using orthoimage pairs to evaluate displacement rates involves a source of uncertainty linked to the errors in the orthoimages used for the surface feature tracking process. The main source of uncertainty is due to local distortions of orthoimages.
Spatial distribution of displacement rates. For local site analyses and where high quality aerial images are available, the spatial distribution of the displacement rates was computed using the IMCORR algorithm. The software output consisted of a point grid of estimated displacements and directions. After manual filtering of output errors (e. g. unrealistic displacements due to shading and/or snow), the point grid was interpolated using the inverse distance weighted method to produce a displacement rate heatmap (Supplementary Figs. 1, 6, 10, 15, 17).
Data Climatic data. Climate is identified as the air temperature anomaly since the end of the preindustrial era (i. e. 1850) and it is evaluated at each rock glacier location using climate datasets for the French Alps. For the 1958-2018 period, data are extracted from the SAFRAN reanalysis, which operates for 23 massifs in the French Alps, and within which atmospheric conditions, in particular temperature, are assumed to be homogeneous and vary with elevation using 300 m elevation steps  44. For earlier periods, the HISTALP gridded data (1 km cell) were used. They consist of an interpolation of weather station data in the European Alps since 1780  45. To combine these two data sets, the overlapping period between SAFRAN and HIS-TALP data (1958-2014) was used to compute a linear model to extrapolate the SAFRAN data in the past from 1850 to 1957.
This is done for each period where displacement rate pairs were available on the orthoimages. Low strain rate values in destabilised rock glaciers refer to the deceleration of the destabilised area (Phase IV-Fig. 1 ). The boxplot respect the standardized sizes: the interquartile (IQR) range groups observations from the 25th (Q1) to 75th (Q3) percentile, while the whiskers represent the maximum (Q3 + 1. 5*IQR) and minimum (Q1-1. 5*IQR). the two data sets (SAFRAN and HISTALP) varies from 0. 4 to 0. 9, depending on the rock glacier site. Displacement rates. The displacement rate analysis is based on orthoimages originating from three sources: (i) orthomosaics produced by the National Institute of Geography (IGN) for the entire French territory, (ii) local orthoimages produced using aerial images acquired by the IGN photogrammetric missions since the late 40s, and (iii) local orthoimages produced using in-situ UAV photogrammetry, performed in 2017 and 2018.
The regional-scale analysis was based only on the first data source, while the local-scale analysis benefits from the second data source as well. UAV photogrammetry was conducted only on four destabilised rock glaciers (PB, RN, CL and TL). For a summary of available orthoimages and their characteristics used in the local-scale assessment, see the supplementary materials. The orthoimosaics provided by the IGN consist of assembled orthoimages of each administrative department collected over a specific time period. The orthoimages in an orthomosaic can, therefore, be collected in different years. The Supplementary materials provide a synthesis of orthoimage dates per administrative department. The advantage of this data source is that it is ready-touse data at relatively high quality at the regional scale.
This allows to focus on gathering displacement rate data rather than producing orthoimages at the regional scale. For each rock glacier in the region, four orthomosaics for the years 1948-1952, 2001-2004, 2008-2009 and 2015-2017 are observable. This allows us to compute displacement rates over the three periods referred here as: Period 1 (second half of the 20th century), Period 2 (first decade of the 21st century) and Period 3 (Late 2000s to mid 2010s). Aerial images have been regularly acquired by the IGN since the late 1940s, providing a valuable database for diachronic studies. This data source enables a significant increase in the temporal resolution of the analysis of the study sites. The aerial images are treated using the classic Structure from Motion (SfM) approach in Agisoft Photoscan  58.
Ground control point (GCPs) coordinates to georeference the model were acquired from a master frame, i. e. the 2018 UAV orthoimage (described in the section below) or the 2015-2017 IGN Orthoimage when UAV data is unavailable. GCPs are collected around the perimeter of the rock glacier to increase the accuracy of the resulting orthoimage within the landform area. The number of GCPs number is initially kept at its minimum for the georeferencing and optimisation process, i. e. three points, and increased if the resulting orthoimage does not present a suitable accuracy when compared to the master frame. Because UAV photogrammetric missions are capable of providing high quality orthoimages and terrain models for rock glaciers  59, 60  , we conducted two surveys in summer-fall 2017 and 2018 at the PB, TL, RN and CL rock glacier sites.
Images were acquired using the commercial UAV Mavick Pro and the automatic flight planner Drone Map. Except for the survey at PB in 2017, where the flight planner failed, all the surveys were conducted acquiring images with an overlap of 85% at an altitude of at least 90 m. For the 2018 session, a second flight level at 150 m altitude was added to the mission. UAV pictures were treated using the classic SfM approach in Agisoft Photoscan. High precision GCPs were acquired only in 2018 using a Trimble GPS as a rover and a Trimble as a base. The method chosen was rapid-static and the raw data were post-processed in the differential mode. This allowed us to compute and georeference the DEMs and orthoimages for the 2018 survey and use their fixed areas as GCPs to reference the models from the 2017 surveys.
Acknowledgements This study was funded by the  European Regional Development Fund  ( POIA PA0004100 ) grant and by the  French National Research Agency  in the framework of the  Investissements d'Avenir programs: Risk@UGA  ( ANR-15-IDEX ). SAFRAN reanalysis data were provided by the  CEN (Centre Etudes de le Neige) ,  MétéoFrance Grenoble. The authors wish to thank  Lucie Berenguer ,  Etienne Berthier ,  Samuel Morin  and  Baptiste Vandecrux ,  Robert Kenner ,  Adriano Ribolini  and four anonymous reviewers for data treatment and for providing high quality feedback of the manuscript, improving the study.
Limitations Our study has some limitations. We used electronic surveys to facilitate the access to our images of interventionalists from different centers. Not all images of the original DSA were available to the reviewers, and not all cases had data regarding collateral circulation. Data regarding the time from onset and time to recanalization were missing. This issue may have affected the reviewers' judgment regarding brainstem areas at risk. Last, our study did not test intrarater agreement, namely whether images with a defined "true score" are consistently graded similarly by the same grader, but this agreement would greatly complement the present study. Given that there is no "true value" for a particular case, the best surrogate that the authors can envision is a large consensus of experts-if it can be reached.
The rck open reading frame (ORF) on the pefI-srgC operon encodes an outer membrane protein responsible for invasion of nonphagocytic cell lines and resistance to complement-mediated killing. Until now, the rck ORF was only detected on the virulence plasmids of three serovars of Salmonella subsp. enterica (i. e. , Bovismorbificans, Enteritidis, and Typhimurium). The increasing number of Salmonella genome sequences allowed us to use a combination of reference sequences and whole-genome multilocus sequence typing (wgMLST) data analysis to probe the presence of the operon and of rck in a wide array of isolates belonging to all Salmonella species and subspecies. We established the presence of partial or complete operons in 61 subsp. enterica serovars as well as in 4 other subspecies with various syntenies and frequencies.
We took advantage of the large number of Salmonella genome assemblies available in the public database Enterobase to perform a large-scale screening of the pefI-srgC ORFs  (25). One or more of these ORFs were found in 67,596 genomes associated with 61 serovars of S. enterica subsp. enterica but also in subsp. salamae, arizonae, diarizonae, and houtenae. The rck ORF was retrieved in 36 of them, thus extending greatly the list of serovars encoding this virulence factor. As a polymorphism at the rck locus was observed, in vitro studies were also carried out to assess the functionality of new Rck variants concerning their ability to confer complement resistance and invasion capacity to the pathogen.
RESULTS Analysis of the pefI-srgC operon within Salmonella reference genomes. In order to find new strains carrying the pefI-srgC operon, we first looked for the presence of the operon's ORFs in the genomes of reference strains. A BLAST search of all these ORFs on 27 genomes retrieved from the ATCC genomic database allowed us to extend the list of pefI-srgC 1 serovars and highlighted variations in the composition and organization of the pefI-srgC operon according to the serovar. As stated by  Mambu et al. (4) , two observations can be made when comparing the three complete operons previously described (retrieved on the virulence plasmids of S. Typhimurium, S. Enteritidis, and S. Bovismorbificans). The first observation is that the structure of the operon varies between these three serovars, as an inversion was observed between the pefI-srgD locus and the srgA ORF in S.
Enteritidis and S. Bovismorbificans remain currently unknown. The complete pefI-srgC operon was not found in any other tested genomes retrieved from the ATCC database. However, several genomes harbored one to four ORFs of the operon. The sequence found on the S. Paratyphi C virulence plasmid (pSPCV) carries pefI, srgD (orf7), and truncated srgB ORFs and is very similar to the corresponding virulence plasmid sequence of S. Enteritidis (pSLA5), especially the upstream region that displays a very similar synteny and high homology ($97%), suggesting a similar regulatory scheme and a common origin (Fig. 1A ). S. Dublin, S. Paratyphi A, and S. Typhi genomes also carry a part of the operon (i. e. , 59-pefI-srgD-srgA-srgB-39), lacking the two last ORFs rck and srgC. These partial operons are located on the chromosome of their respective strains near the sef (S.