dataesr/scientific-paragraphs-categorization huggingface dataset:
The 16S sequences were retrieved from genome sequences using RNammer v1. 2  [110]  (options -S set to bac and them to ssu). We aligned separately archaeal and bacterial 16S rRNA using the secondary structure models with SSU_Align v0. 1. 1 (http://eddylab. org/software/ssualign/, default options). Poorly aligned positions were masked with ssu-mask. The alignment was trimmed with trimAl v1. 4rev15  [111]  (-noallgaps, that allow to remove from the alignment regions that are only composed of gaps). The maximum likelihood trees were inferred using IQ-TREE v1. 6. 7. 2  [51]  (using the best-selected model SYM+R6 for the archaeal tree and SYM+R10 for the bacterial tree, -bb 10000 ultrafast bootstrap  [108] , -wbtl to conserve all optimal trees and their branch lengths).
Reference systems dataset The dataset with all the systems identified in the genomes is too large to make phylogenetic inferences. It also contains many very closely related systems that may provide little additional information to infer the deeper nodes of the tree. Hence, we developed a method to remove redundancy in the dataset while maximising its genetic diversity. The method prioritizes the inclusion of systems that were experimentally validated to facilitate the analysis of the results. The method consists of several sequential steps (Figure  S15 ). 1) We inferred the maximum likelihood tree for each key components' family as mentioned above and extracted the matrices of patristic distances (using the R function "cophenetic_phylo" of the package ape) between all leaves of the trees.
De-replicated dataset To reduce the number of paralogs in each system, we used the following method (Fig. S16 ). 1) We inferred the maximum likelihood tree for each key components' family of the representative dataset as mentioned above and extracted the matrices of patristic distances (using the R function "cophenetic_phylo" of the package ape) between all leaves of the trees. This resulted in a set of distance matrices between proteins. 2) For each system with more than one copy per gene, we found the nearest system based on patristic distances extracted from the ATPase or the integral membrane platform tree (depending on the number of copies of the ATPase), which had only one copy of this gene. 3) We use this nearest system to choose the copy of the duplicate gene with the smallest distance to its homolog in this nearest system.
The proteins of each cluster were aligned with MAFFT and used to build HMM protein profiles as described above. To annotate the protein clusters we used two methods. First, we searched for similarities of their HMM profiles with the profiles used to identify the TFF-SF components using HHsearch (v3. 0. 3, p-value < 10 -5 ). Second, we searched for homologies between the remaining clusters and the profiles of the PFAM database (v31. 0, same method). To test if a given cluster is significantly positively associated with a given type of system, we made the following analysis. We counted the occurrences of the elements of the cluster associated with a given type and made a contingency table where the columns are the type versus all other types and the lines are presence or absence of an element from the cluster.
Acknowledgements We are much indebted to  Olivera Francetic  for comments, encouragement and guidance during the development of this study. We thank  Simonetta Gribaldo  for comments and suggestions on a previous version of the manuscript,  Bertrand Néron  for continuous support of MacSyFinder. We also thank  Panagiotis Adam  for providing a comprehensive phylogenetic tree of the archaeal and bacterial domains to be used as support for Figs. 4,  S2  and  S11.
We present an adaptation of the Lagrangian stochastic dispersion model of the computational fluid dynamics (CFD) open source code Code Saturne to simulate atmospheric dispersion of pollutants in complex urban geometries or around industrial plants. The wind is modeled within the same code with an Eulerian RANS (Reynolds-averaged Navier-Stokes equations) approach and thus involves the solution for the ensemble-mean velocity field and turbulent moments, using eddy viscosity or Reynolds stress turbulence models adapted to the atmosphere and complex geometries. The Lagrangian stochastic model used for the dispersion of the particles within this flow field is the simplified Langevin model, which pertains to the approaches referred to as PDF (Probability Density Function) methods. This formulation of model has not been widely used in atmospheric applications, despite interesting theoretical and computational benefits.
(2005a,b) ,  Bernardin et al. (2009) ,  Alessandrini and Ferrero (2009) ,  Tinarelli et al. (2013) , etc. For our work, a simulation tool using a Lagrangian PDF (Probability Density Function) to carry out pollutant dispersion studies has been developed in the three-dimensional CFD code Code Saturne (http://code-saturne. org/, see  Archambeau et al. (2004)  for more details). It has been validated on simple academic cases and shown to satisfactorily respect the well-mixed condition (see  Bahlali et al. (2018a,b) ). This paper is concerned with validating the model in real conditions by studying continuous point source dispersion of a non-reactive pollutant in an idealized urban area, as such a case is typical of industrial emissions or an accidental release. The present work will focus on the Mock Urban Setting Test (MUST) campaign, which has been widely studied in the literature.
For previous numerical simulations of the experiment, the reader may for example refer to  Hanna et al. (2004) ,  Camelli et al. (2005) ,  Donnelly et al. (2009) ,  Antonioni et al. (2012) ,  Kumar et al. (2015). Comparisons between different modeling systems have also been studied:  Santiago et al. (2010)   In the CFD code Code Saturne, previous numerical simulations of the MUST campaign have also already been performed by  Milliez and Carissimo (2007, 2008)  and used an Eulerian k -model of turbulent dispersion. The present work aims at studying the same cases using the Lagrangian stochastic model. The objective is twofold: • first, we would like to assess the accuracy of the results provided by the Lagrangian model for this specific industrial application; • second, we would like, in the same CFD simulations, to compare the Lagrangian results to the ones obtained with several Eulerian turbulence models, and above all explain the differences to provide a better understanding of the different modeling options.
Description of the site The experimental program this paper focuses on is the Mock Urban Setting Test campaign, conducted in Utah's desert, USA, by the US Defense Threat Reduction Agency (DTRA). It consists of the release of a pollutant in an idealized urban environment represented by several rows of containers. The details of the experiment as well as the results are described in  Biltoft (2001)  and  Yee and Biltoft (2004). It is worth noting that pollutant dispersion simulations in urban geometries using CFD have been widely studied in the past few years, for instance within street canyons  (Tominaga and Stathopoulos, 2011; Salim et al. , 2011)  or around high-rise buildings  (Yoshie et al. , 2011). In this work, the objective is to reproduce some of the simulations of the MUST campaign that have already been performed in the past by  Milliez and Carissimo (2007, 2008)  in Mercure (former name of the atmospheric module of Code Saturne), but completing them with new results using the Lagrangian stochastic model previously introduced and the second-order R ij -model for the fluid phase computation.
Conclusions This work aimed at assessing the ability of a Lagrangian stochastic model to perform point source dispersion in an idealized urban area, for neutral and stable meteorological conditions. The Lagrangian model used was a model based on  Pope (2000) 's SLM and  Minier and Peirano (2001) 's two-phase flow formulation. Pope's PDF models have gone quite unnoticed in the atmospheric dispersion community, despite presenting some nonnegligible theoretical and numerical advantages (see  Bahlali et al. (2018a,b) ). As used within a hybrid Eulerian/Lagrangian approach, the Lagrangian solver was fed by the mean dynamical fields provided by the Eulerian solver of the same CFD code Code Saturne. These fields were computed either through a k -model or a second-order R ij -model. The Lagrangian results were compared to the Eulerian ones, for both turbulence models.
In the permafrost context, this implies being able to accurately represent (i) the source, reactivity, and transformation of released organic matter; and (ii) the dynamic response of hydrological processes to warming, since water phase determines carbon, heat, and soil moisture availability for metabolisation and lateral transport. For this purpose, we take a specific version of the terrestrial component of the Institut Pierre Simon Laplace (IPSL) global Earth system model (ESM) ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), one that is specifically coded for, calibrated with, and evaluated on high-latitude phenomena and permafrost processes, called ORCHIDEE-MICT (where MICT stands for aMeliorated Interactions between Carbon and Temperature;  Guimberteau et al. , 2018). This code is then adapted to include DOC production in the soil (ORCHIDEE-SOM;  Camino-Serrano et al.
Methods This section overviews the processes represented in the model being described in this paper, which is referred to as ORCHIDEE MICT-LEAK, hereafter referred to MICT-L for brevity. MICT-L is at its heart a merge of two distinct models: the high-latitude land surface component of the IPSL Earth system model ORCHIDEE MICT and the DOC production and transport branch of ORCHIDEE's default or "trunk" version  (Krinner et al. , 2005) , ORCHILEAK. The original merger of these two code sets was between OR-CHILEAK and ORCHIDEE-MICT, which are described in  Camino-Serrano et al. (2018) ,  Lauerwald et al. (2017) , and  Guimberteau et al. (2018). However, numerous improvements in code performance and process additions postdating these publications have been included in this code. Furthermore, novel processes included in neither of these two core models are added to MICT-L, such as the diffusion of DOC through the soil column to represent its turbation and preferential stabilisation at depth in the soil, as described in Sect.
2. 11. In terms of code architecture, the resulting model is substantially different from both of its parents owing to the fact that the two models were developed on the basis of OR-CHIDEE trunk revisions 2728 and 3976 for ORCHILEAK and MICT, respectively, which have a temporal model development distance of over 2 years and subsequently evolved in their own directions. These foundational differences, which mostly affect the formulation of soil, carbon, and hydrology schemes, mean that different aspects of each are necessarily forced into the subsequent code. Where these differences were considered scientific or code improvements, they were included in the resulting scheme. Despite architectural novelties introduced, MICT-L carries with it a marriage of much the same schemes detailed exhaustively in  Guimberteau et al.
Thus, a reduction factor which reduces the vertical advection of DOC in soil solution by 80 % compared to the advection is applied to represent a slowdown in DOC percolation through the soil and increase its residence time there. In MICT-L, as in ORCHILEAK, a "poor-soil" module reads off from a map giving fractional coverage of land underlain by Podzols and Arenosols at the 0. 5 • grid scale, as derived from the Harmonized World Soil Database  (Nachtergaele et al. , 2010). Due to their low pH and nutrient levels, areas identified by this soil-type criterion experience soil organic matter decomposition rates half those of other soils  (Lauerwald et al. , 2017) , as derived from  Bardy et al. (2011) ,  Vitousek and Sanford (1986) , and  Vitousek and Hobbie (2000). To account for the very low DOC-filtering capacity of these coarse-grained, base, and clay-poor soils (DeLuca and Boisvenue, 2012, Fig.
Routing scheme The routing scheme in ORCHIDEE, first described in detail in  Ngo-Duc et al. (2007)  and presented after some version iterations in  Guimberteau et al. (2012) , is the module which, when activated, represents the transport of water collected by the runoff and drainage simulated by the model along the prescribed river network in a given watershed. In doing so, its purpose is to coarsely represent the hydrologic coupling between precipitation inputs to the model and subsequent terrestrial runoff and drainage (or evaporation) calculated by it, on the one hand, and the eventual discharge of freshwater to the marine domain on the other. In other words, the routing scheme simulates the transport of water by rivers and streams by connecting rainfall and continental river discharge with the land surface.
These floodwaters may then either process the DOC directly, via oxidisation to CO 2 (Sect. 2. 10, 2. 11), or return them to the river network as floodwaters recede to the river main stem, at which point they join the runoff and drainage export flows from upstream. MICT-L includes the floodplain hydrology part of the routing scheme (d  'Orgeval et al. , 2008; Guimberteau et al. , 2012) , as well as additions and improvements described in  Lauerwald et al. (2017). The spatial areas that are available for potential flooding are predefined by an input map originally based on the map of  Prigent et al. (2007). However, for this study, we used an alternative map of the "regularly flooded areas" derived from the method described in  Tootchi et al. (2019) , which in this study uses an improved input potential flooding area forcing file specific to the Lena basin that combines three high-resolution surface water and inundation datasets derived from satellite imagery: GIEMS-D15  (Fluet-Chouinard et al.
, 2015) , which results from the downscaling of the map of  Prigent et al. (2007)  at 15 arcsec (ca 500 m at the Equator); ESA-CCI land cover (at 300 m ∼ 10 arcsec); and JRC surface water at 1 arcsec  (Pekel et al. , 2016). The "fusion" approach followed by this forcing dataset stems from the assumption that the potential flooding areas identified by the different datasets are all valid despite their uncertainties, although none of them are exhaustive. The resulting map was constructed globally at 15 arcsec resolution, and care was taken to exclude large permanent lakes from the potential flooding area based on the HydroLAKES database  (Messager et al. , 2016). In the Lena River basin, the basin against which we evaluate ORCHIDEE MICT-LEAK in Part 2 of this study, this new potential floodplain file gives a maximum floodable area of 12.
This is potentially significant, since the vertical diffusion of relatively reactive matter may strongly impact (accelerate) the decomposition of low-reactivity matter in the deeper non-frozen horizons of high-latitude soils, while the explicit discretisation of the soil column is a significant improvement in terms of the accuracy of process representation within the column itself. Anther carbon-relevant scheme included in MICT-L is a prognostic fire routine (SPITFIRE) calibrated for the trunk version of ORCHIDEE  (Yue et al. , 2016) , which is available in our code but not activated in the simulations conducted here. As a result, we do not simulate the ∼ 13 % of Arctic riverine DOC attributed to biomass burning by  Myers-Pigg et al. (2015)  or the ∼ 8 % of DOC discharge to the Arctic Ocean from the same source  (Stubbins et al.
, 2017). Likewise, a crop harvest module consistent with that in ORCHIDEE-MICT exists in MICT-L but remains deactivated for our simulations. A module introduced in the last version of ORCHIDEE-MICT  (Guimberteau et al. , 2018) , in which the soil thermal transfer, porosity, and moisture are strongly affected by SOC concentration, is deactivated here because it is inconsistent with the new DOC scheme. Specifically, while carbon is conserved in both the MICT and MICT-L soil schemes, MICT-L introduces a new reservoir into which part of the total organic carbon in the soil -the DOC -must now go. This then lowers the SOC concentration being read by this thermix module, causing significant model artefacts in soil thermodynamics and hydrology in early exploratory simulations. Ensuring the compatibility of this routine with the DOC scheme will be a focal point of future developments in MICT-L.
Soil carbon spin-up and simulation protocol The soil carbon spin-up component of ORCHIDEE, which is available to both its trunk and MICT branches, was omitted from this first version of MICT-L owing to the code burden required for ensuring compatibility with the soil carbon scheme in MICT-L. However, because we are simulating high-latitude permafrost regions, having a realistic soil carbon pool at the outset of the simulations is necessary if we are to untangle the dynamics of SOC and DOC with a changing environment. Because the soil carbon spinup in ORCHIDEE-MICT is normally run over more than 10 000 years  (Guimberteau et al. , 2108)  and because running MICT-L for this simulation period in its normal, non-spin-up simulation mode would impose an unreasonable burden on computing resources, here we directly force the soil carbon output from a MICT spin-up directly into the restart file of a MICT-L simulation.
A 20 000-year spin-up loop over 1961-1990 (these years were chosen to mimic coarsely warmer mid-Holocene climate) forced by GSWP-3 climatology, the configuration of which derives directly from that used in  Guimberteau et al. (2018) , was thus used to replace the three soil carbon pool values from a 1-year MICT-L simulation to set their initial values. A conversion of this soil carbon from volumetric to areal units was applied owing to different read-write standards in ORCHILEAK versus ORCHIDEE-MICT. This artificially imposed, MICT-derived SOC stock would then have to be exposed to MICT-L code, the large differences of which in terms of soil carbon module architecture com-  Due to the long residence times of the passive SOC pool, reaching full equilibrium requires a simulation length on the order of 20 000 years -again an overburden.
Acknowledgements. Simon P. K. Bowring  acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under  Marie Skłodowska-Curie  grant agreement no. 643052 , " C-CASCADES " programme. Simon P. K. Bowring  received a PhD grant. Matthieu Guimberteau  acknowledges funding from the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. Ronny Lauerwald  acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 703813  for the  Marie Skłodowska-Curie European Individual Fellowship "C-Leak ". Financial support. This research has been supported by the  Marie Skłodowska-Curie ESR  grant ( C-CASCADES  (grant no. 643052 )), the  Marie Skłodowska-Curie European Individual Fellowship  ( C-Leak  (grant no.
Iceberg detection and size characterization is further hindered by the presence of clouds in visible to near-infrared satellite imagery. Excluding images with partial cloud cover would considerably limit the temporal resolution of any derived dataset, resulting in potential aliasing of changes in iceberg size distributions over time. Therefore, efficient mapping of icebergs mandates that clouds are automatically distinguished from snow and ice in satellite images (i. e. automated cloud masking). The primary challenge in mapping clouds in satellite images is that clouds and snow/ice share similar spectral properties across a large portion of the electromagnetic spectrum typically sensed by satellite platforms. Clouds and snow/ice are especially similar in the visible wavelengths (highly reflective) and thermal wavelengths (cold), properties that often allow clouds to be readily distinguished from relatively warm and dark-colored land surfaces.
Dozier (1989) ,  Riggs and Hall (2002) ,  Irish and others (2006) ,  Jedlovec (2009)  and many others provide insight on various approaches to generating cloud masks for optical satellite images, including advanced image analysis techniques based on machine learning  (Lee and others, 1990; Scaramuzza and others, 2012; Hughes and Hayes, 2014)  and edge detection  (Zhu and Woodcock, 2012). To generate a dataset that captures changes in iceberg size distributions, we developed a Landsat-based semi-automated iceberg delineation algorithm that incorporates a machine learning-based cloud mask. We present details of the algorithm and results from Disko Bay, West Greenland, to demonstrate the utility of the algorithm to explore temporal changes in iceberg size distributions commensurate with glacier change.
Our method was developed so that it can be implemented locally on a standard computer and with minimal user input, subsequent to the one-time development of appropriate regional input files. We validate our cloud mask through manual analysis and evaluation of model output against a validation dataset for Disko Bay, and iceberg detection is validated using visual inspection and an independent application in Kangerlussuup Sermia Fjord. Finally, we compare changes in iceberg size distributions in Disko Bay between 2000-02 and 2013-15 to explore the use of iceberg size distribution time series as a means to infer changes in glacier dynamics. We find that the ice cover and iceberg size distributions change over time concurrent with a shift in parent glacier calving style.
3b ). We also tested a ratio of the near-infrared (NIR) to SWIR bands (centered at ~0. 8 and 1. 6 μm, respectively), ultimately generating a ratio-based cloud mask using a combination of the NIR: SWIR ratio and SWIR reflectance (Fig. 3c ). The results produced using the red-SWIR normalized index were similar to those of the individual SWIR reflectance and red:SWIR tests (not shown). The performance of each ratio-based method to distinguish clouds from snow/ice varied widely with cloud type. Very bright, white, fluffy cumulus clouds, which often had similar reflectance values as icebergs, resulted in enough false identifications of clouds and/or icebergs to render the dataset meaningless. The combination of the NIR:SWIR ratio and SWIR reflectance to identify clouds improved the differentiation between clouds and snow/ice over either method alone and over other combinations of the ratio and threshold methods described above (Figs  3b, c ) and was thus used as a point of comparison for the performance of our machine learningbased cloud mask.
Random seeding is also not feasible because it would require dense enough coverage to include a seed in each iceberg and cloud segment (where clouds are present); this would likely lead to over-segmentation of the image. Given the poor performance of the edge detection methods for distinguishing clouds and ice, they were not pursued further. Machine learning-based masks. To construct the cloud masks ultimately included in our algorithm, we applied a machine learning technique called multinomial logistic regression (Fig. 3e ), wherein a classified training set is provided as input and the computer generates a series of functions to relate the image pixel values and their classifications. Then, the model is run on validation datasets and the computer-predicted values are compared to the actual values.