dataesr/scientific-paragraphs-categorization huggingface dataset:
NMDS was generated using the metaNMDS function. Ordiellipse option was used to display the variance observed per group, and PERMANOVA tests were performed using the pairwise. adonis and vegdist functions (Bray-Curtis dissimilarities) to confirm the significance of differences between groups. The non-subsampled 16S rRNA gene reads genus table (Supplementary Table  6 ) was processed with the DESEQ2 package  (Love et al. , 2014)  in order to reveal genera with significant change in abundance before and after treatment. Counts normalized by the median of ratios method  (Anders and Huber, 2010)  were recovered and relative abundances were calculated in order to present the percentage of significant differences in abundances at the genus level before and after treatment. Change in abundance has been considered significant if a genus shows a log2 fold change ≤ -2 or ≥ +2 with a minimum Base Mean value of 10 and adjusted p value of < 0.
05. To facilitate 1 https://www. arb-silva. de/documentation/ global comparison with other studies, computations were also performed at the phylum level. All the functions used without package indication were from the vegan package  (Oksanen et al. , 2019)  and all analyses were performed on RStudio  (R v3. 3. 3). The statistical experimental design (Kruskal-Wallis tests on 16S rRNA diversity indices and MST markers, as well as NMDS and DESEQ2 analyses) implied first testing relations according to treatment (AD against composting), regardless of the OW origin and regardless of the post-treatments. Thus, all OW samples were compared to composted samples and digestates. Second, the OW origins were considered in the comparisons to infer their impact on the V5-V6 16S rRNA gene datasets. The 16S rRNA gene sequences produced in this study are available from webin under the accession number PRJEB40193.
General Chemical and Microbiological Features of the Raw OWs, Digestates, and Composts Principal component analyses were performed on the physicochemical datasets (organic carbon, total nitrogen, C:N ratio, pH, and phosphorus) and pollutants (iron and zinc concentrations, PAHs, NPs, and TE) measured on all samples (Supplementary Tables  1, 2  and Supplementary Figure  1 ). The first two axes of these PCAs explained a significant part of the variability observed among these datasets (>60%) (Supplementary Figure  1 ). The origin of the sample was found to explain the chemical dataset (Supplementary Figure  1 ). The urban samples (treated or not) were significantly differentiated from the agricultural and central (mixed) samples by a set of pollutants including several TE (Ag, Al, As, Cd, Ce, Co, Cr, Cu, Ga, K, Mo, Ni, P, Pb, and Ti), 4-nonylphenol (A2), phenanthrene (H5), anthracene (H6), and total nitrogen concentrations (KW-Dunn's post hoc tests; p < 0.
05) (Supplementary Figure  2 ). Similarly, global indicators of the microbiological content of the samples were analyzed by qPCR using microbial source tracking DNA targets (Supplementary Table  4  and Figure  1 ). These qPCR analyses showed that 16S rRNA gene numbers varied from 4. 26 × 10 5 copies/g sample up to 6. 41 × 10 11 copies/g (Supplementary Table  4 ). Lowest 16S rRNA gene copy numbers/g sample were obtained for digestates. Monitorings of total Bacteroides cell numbers were correlated with the 16S rRNA gene dataset (Kendall's rank correlation tau; p < 0. 01) but the range of values was lower (2. 44 × 10 2 copies/g sample up to 1. 12 × 10 9 copies/g). The DNA indicators for pig and bovine fecal contaminations were most significant among central and agricultural raw OW (Figure  1  and Supplementary Table  4 ).
These distributions were in agreement with the information given by the operators of the OW treatment sites. The human DNA fecal marker was most often detected among the raw OW but in significantly higher numbers (KW and Dunn's post hoc; p < 0. 05) among the urban and central groups of samples (Supplementary Table  4  and Figure  1 ). Digestates and composts showed lower occurrences of these markers but   4. Numbers of (A) HF183 and (B) pig-2-bac DNA copies per gram dry weight organic wastes, composts, or digestates normalized to 16S rRNA gene copy numbers were presented according to OW origins (agricultural, urban, and central). Boxplots show the minimum, first quartile (Q1), median, third quartile (Q3), and maximum relative abundances observed among the datasets. Outliers or unexpected values are indicated by dots.
Samples used in these analyses are those of Table  1. a significant number of HF183 DNA was detected among the urban digestates (Figure  1 ). Bacterial Diversity of Raw OWs, Digestates, and Composts Inferred From V5 to V6 16S rRNA Gene Sequences About 5 million DNA sequences were obtained for the samples investigated in this work, and 45% were validated as highquality 16S rRNA gene sequences. The number of high-quality V5-V6 16S rRNA gene sequences per sample ranged from 26,465 to 119,793 (Supplementary Table  7 ). The number of reads per sample were negatively correlated to the 16S rRNA gene qPCR dataset (Spearman Rank Correlation; rho = -0. 45, p < 0. 05). This highlighted a variability in the dataset that required a normalization of the 16S rRNA gene dataset prior to performing statistical analyses.
The global representation of the dataset and the computing of diversity indices were thus performed after a subsampling of the gene reads at the lowest value for the number of reads on a sample, i. e. , 26,465 reads. This subsampled dataset led to a grouping of reads into 7755 OTUs, which varied in their distribution from 318 to 1404 per sample (Supplementary Table  5 ). Accordingly, Shannon diversity, Shannon-evenness, and the Simpson diversity indices were computed (Supplementary Table  7  and Supplementary Figure  3 ). Significant differences in these indices were observed between those computed for the urban digestates and those of the agricultural or central samples (KW test; p < 0. 05). More diverse OTU contents and even distributions of the reads between OTUs were observed among the urban digestates than the other ones.
Incidence of AD or Composting on Bacterial Taxa as Inferred From 16S rRNA Gene Taxonomic Allocations The chemical and V5-V6 16S rRNA gene OTU pattern analyses indicated in the previous sections showed (i) a significant proximity between the agricultural and central samples (raw OW and digestates), (ii) a significant differentiation of the urban samples (raw OW and digestates) from the agricultural/central samples, and (iii) a significant differentiation of the compost samples from all the other samples. Accordingly, in the following analyses, the datasets were grouped into these latter three entities. Informative groupings of V5-V6 DNA reads matching significant changes (higher or lower) in relative abundances between these entities were analyzed.
9%; equivalent to 1. 01 × 10 10 16S per gram) (Supplementary Table  9  and Figure  3 ). These urban OWs were found to have higher relative counts of Proteobacteria (46. 6%; equivalent to 3. 97 × 10 10 16S per gram) and Actinobacteria (11. 7%; equivalent to 6. 51 × 10 9 16S per gram) than the central and agricultural samples (respectively, 29. 3%; equivalent to 8. 74 × 10 9 16S per gram and 2. 9%; equivalent to 1. 2 × 10 9 16S per gram) (Supplementary Table  9  and Figure  3 ). It is to be noted that 16S rRNA gene reads allocated to the Nitrospirae, Chloroflexi, and Acidobacteria represented more than 1% (equivalent to 7. 41 × 10 9 16S rRNA gene copies per gram) of the urban OW dataset, but these phyla occurred at a much lower score (equivalent to 4. 32 × 10 6 16S rRNA gene copies per gram) among the OW of the other sites (Supplementary Table  9  and Figure  3 ).
Firmicutes showed the highest occurrence among the central/agricultural digestates (52. 2% of the bacterial taxa). These relative changes matched an averaged one log increase in Firmicutes 16S rRNA gene copies between the raw OW and the digestates of the urban sites or composts (Supplementary Table  9  and Figure  3 ). Similarly, relative increases in Actinobacteria, Deferribacteres, and Thermotogae among composts matched significant increases in 16S rRNA gene copies estimated from the qPCR datasets (Supplementary Table  9  and Figure  3 ; KW p ≤ 0. 05). Similar trends were also observed between raw OW and urban digestates for the Cloacimonetes, which showed up to a five-log increase in 16S rRNA gene copies per gram. Significant decreases in the relative abundance of some phyla were also recorded such as the raw OW Fusobacteria, which became negligible among the digestates and composts (Supplementary Table  9  and Figure  3 ; KW p ≤ 0.
Variations at the Scale of Genera Among Digestates To go deeper into the understanding of community changes, variations of V5-V6 16S rRNA gene reads allocated at the genus level were investigated. A DESEQ2 analysis was performed to highlight these changes (Figures  4, 5  and Supplementary Tables  10, 11 ). These analyses were performed with the nonresampled sequenced gene reads dataset (Supplementary Table  6 ) because a normalization scheme is directly run by DESEQ2. This approach revealed that the digestates of the central/agricultural category of samples led to a significant increase in the relative proportion of six genera: Gelria (Firmicutes), Sporosarcina (Firmicutes), Dethiobacter (Firmicutes), and three unclassified groups belonging to the Spirochaetales, Cloacimonetes, and Firmicutes.
The decrease in bacterial numbers was confirmed through a conversion of the dataset into 16S rRNA gene copies. Lactobacillus 16S rRNA gene copies went down by about two logs, and those of Bacteroides went down by about one log (supported by Wilcoxon rank sum tests; p < 0. 05). Similar trends were observed for the other genera (Supplementary Table  9 ). Several bacterial genera of the urban OWs were shown to be significantly enriched in the digestates. A total of 32 genera (of which 25 were part of the core microbiome) showed a significant increase (Figure  5  and Supplementary Table  11 ). These genera were enriched by three to sevenfold while comparing with the concentrations observed among OW. These genera represented from 0. 03 to 3. 6% of the total digestate microbial community. The most significant increases in the digestates were observed for V5-V6 16S rRNA gene reads allocated to the Smithella (0.
These decreases were about four-to ninefold in comparison to their relative numbers among the raw OW samples. Hence, the Uliginosibacterium (1. 3-0. 1%), Simiduia (0. 3-0. 0%), Cellvibrio (0. 1-0. 01%), Fonticella (0. 1-0. 0%) Polyangium (0. 04-0. 0%), and Clostridium_sensu_stricto_12 (0. 03-0. 0%) genera showed such a decrease (Figure  5  and Supplementary Table  11 ). These shifts were confirmed while converting these datasets into 16S rRNA gene copy numbers (Supplementary Table  9 ). Global changes in the relative abundance of digestates' bacterial genera significantly affected by ADs over the dataset were investigated. DESEQ2 analyses revealed that 30. 8 and 35. 5% of the total relative abundance of the digestate genera were significantly affected by the AD treatment, respectively, at the urban and agricultural/central sites (Supplementary Tables  10, 11 ).
Incidence of OW Digestion Processes on Bacterial Functional Groups Functional traits selected by AD and composting were inferred from the taxonomic allocations performed at the genus level. At first, the sum of relative abundances of enriched genera potentially covering a functional trait extracted from the FAPROTAX database using MACADAM was calculated (Supplementary Table  13 ). The contribution of each genus to differences in hypothetical functional profiles is based on relative abundances before and after treatment (Supplementary Table  13 ). Based on the most abundant taxa in digestates from urban origin, the five main hierarchical functional traits were chemoheterotrophy (6. 3%), fermentation (6. 2%), human and mammal gut colonizers (each feature at 2. 1%), and animal parasites or symbionts (2.
The theoretical metabolic pathways of the genera identified in this study were further inferred from sequenced genomes using the MACADAM software. Metabolic pathways (with a PS ≥ 0. 5) potentially present in genera significantly impacted by the investigated digestion processes were counted, and their relative distribution patterns were compared (Table  2  and Supplementary Table  14 ). Distribution patterns between these functional categories observed among OWs from urban sites and from central/agricultural ones were found positively correlated (Spearman Rank Correlation; rho = 0. 69, p < 0. 05). Such correlations were not observed between the anaerobic and aerobic digestion processes (digestates and composts). After AD treatments, biosynthesis pathways for cofactors, nucleotides, amino acids, lipids, and aminoacyl-tRNAs charging were more abundant in the enriched genera than in the decaying ones whatever the raw OW origin (Table  2  and Supplementary  Table 14 ).
DISCUSSION OW recycling of sewage sludge and livestock effluents represent a major challenge because not only the bioenergy production but also environmental and sanitary issues must be considered. A key criterion in the assessment of the microbiological risks of OW recycling that remain poorly defined is the nature and structure of the various microbiomes getting into these processes and their potential health and ecological hazards. Here, the core and flexible components of the end product microbiomes were inferred, and the likelihood of these at being reproducibly found among highly diverse OW treatment plants was evaluated. Nine full-scale treatment plants distributed over France were selected to obtain a global view of microbiome bacterial taxa in the French OW treatment sector. These inferred bacterial taxa (from metabarcoding 16S rRNA gene datasets) are discussed below according to process type (AD, composting) and OW origins (fecal wastes from WWTP or agricultural sites, and food products).
Key functional traits of the bacterial genera impacted by the digestion processes were investigated to go further with the identification of key structuring bacterial processes. According to NMDS ordinations of 16S rRNA gene metabarcoding datasets, two main bacterial community structures could be identified and found to match a segregation between bacterial taxa from OW containing agricultural wastes or not (restricted to urban WWTP activated sludge). This segregation was also in line with a differentiation between their contents in micropollutants particularly the TEs. The urban samples (treated or not) had significantly more micropollutants including 14 TEs (Ag, Al, As, Cd, Ce, Co, Cr, Cu, Ga, Mo, Ni, P, Pb, and Ti), 4-nonylphenol, and 2 PAHs (phenanthrene and anthracene) than the agricultural and mixed (agricultural/urban; termed "central" in the text) samples.
Bacterial taxa present in agricultural raw OW were outcompeted by the digesters' communities. This could be explained by the pre-treatment conditions performed in municipal wastewater facilities, leading to an increase of specialized bacterial taxa in the activated sludge. These taxa can perform multiple biodegradation processes  (Shchegolkova et al. , 2016)  and can form heterogeneous structures termed flocs  (Ju and Zhang, 2015). Functional inferences through FAPROTAX  (Louca et al. , 2016)  were undertaken to highlight their potential contributions to the AD process. The functional traits of the dominant microbial taxa inferred from the 16S rRNA gene datasets in the AD end products were investigated. Based on FAPROTAX analyses, functional traits such as chemoheterotrophy and fermentation were highly common among the taxa found in digestates of the urban and agricultural/central categories.
Actinobacteria are capable of breaking down a wide range of animal wastes and plant debris particularly complex compounds (e. g. , lignocelluloses), playing an important role in the later stages of composting  (Herrmann and Shann, 1997; Wang et al. , 2016). It is to be noted that thanks to the occurrences of bacterial genera degrading xenobiotics, composting offers the advantage of decreasing the concentration of chemical contaminants  (Semple et al. , 2001; Ghaly. , 2011; Cascant et al. , 2016; Ren et al. , 2018). In line with these reports, genera of xenobiotic degrading Actinobacteria, e. g. , Arthrobacter, were found to be enriched in the 16S rRNA gene datasets generated from composts DNA. To go deeper, closer analyses of functional potentials per taxa enriched after composting were performed.
FUNDING This study was partly funded by the  ANR (Agence Nationale de la Recherche, France)   DIGESTATE  project  ANR-15-CE340003-01 ,  Bacteri ' eau  project  ANR-16-CE04-0014-01 , and the " Auvergne-Rhône-Alpes" Region (France) "pack ambition recherche " project  ITAS-MZ2M.
The speed was calculated either from a unique sequence or as the mean of up to three sequences when possible. We chose sequences of continuous digging (minimum of 6 s to a maximum of 13 s) during which we measured the distance traveled and divided it by the sequence duration to obtain speed (cm/s). We selected sequences where we could easily determine lemming starting and ending points. A scaled picture of the observation box, corrected for distortion with Adobe Lightroom, was used in ImageJ software  (Schneider et al. 2012)  to accurately measure the distance traveled.
All statistical analyses were performed using the R software (R Core Team 2020). We used linear mixed-effects models to examine the relationship between digging speed and either density or hardness of the snow layer, lemming species, and their interactions. Generalized linear mixed-effects models (GLMM) with a gamma distribution and a log-link function were used to handle the variance structure when analyzing the influence of snow type, lemming species, and their interaction on the time spent in different behavior, tunnel length, and time spent under snow during each 30-min trial. The exceptions being the time spent on exploration behavior, which was modeled using linear mixed-effect model, and traveling behavior and time spent under snow, which were modeled using the function VarIdent implemented in the nlme package in R (Pinheiro and Bate 2021), with snow type and species as grouping variables.
ACKNOWLEDGMENTS We thank  Aili Pedersen ,  Spencer Klengenberg ,  Jasmine Tiktalek ,  Ian Hogg , and  Sarah Jacques  for their valuable contribution in taking care of the lemmings in captivity. We thank  Frederick Ross  and  Jean-Marie Trudeau  from the technological instrument development platform of Sentinel North for the conception and fabrication of the lemming observation boxes. We thank  Pierre Legagneux  and  Canadian North  for transportation support to the experimental site. This work was funded by  Sentinel North program  of the  Canada First Research Excellence Fund , the  Fonds de recherche du Qu ebec-Nature  et technologies, the  Natural Sciences and Engineering Research Council of Canada , the  Polar Continental Shelf Program at Natural Resources Canada , the  Fondation de l'Universit e Laval , and the  W.
The intensity relationship that holds between scalar adjectives (e. g. , nice < great < wonderful) is highly relevant for natural language inference and common-sense reasoning. Previous research on scalar adjective ranking has focused on English, mainly due to the availability of datasets for evaluation. We introduce a new multilingual dataset in order to promote research on scalar adjectives in new languages. We perform a series of experiments and set performance baselines on this dataset, using monolingual and multilingual contextual language models. Additionally, we introduce a new binary classification task for English scalar adjective identification which examines the models' ability to distinguish scalar from relational adjectives. We probe contextualised representations and report baseline results for future comparison on this task.
, 2010). Work on scalar adjectives has until now evolved around pre-compiled datasets (de  Melo and Bansal, 2013; Taboada et al. , 2011; Wilkinson and Oates, 2016; Cocos et al. , 2018). Reliance on external resources has also restricted research to English, and has led to the prevalence of pattern-based and lexicon-based approaches. Recently,  Garí Soler and Apidianaki (2020)  showed that BERT representations  (Devlin et al. , 2019)  encode intensity relationships between English scalar adjectives, paving the way for applying contextualised representations to intensity detection in other languages. 1 n our work, we explicitly address the scalar adjective identification task, overlooked until now due to the focus on pre-compiled resources. We furthermore propose to extend scalar adjective ranking to new languages.
We make available two new benchmark datasets for scalar adjective identification and multilingual ranking: (a) SCAL-REL, a balanced dataset of relational and scalar adjectives which can serve to probe model representations for scalar adjective identification; and (b) MULTI-SCALE, a scalar adjective dataset in French, Spanish and Greek. In order to test contextual models on these two tasks, the adjectives need to be seen in sentential context. We thus provide, alongside the datasets, sets of sentences that can be used to extract contextualised representations in order to promote model comparability. We conduct experiments and report results obtained with simple baselines and state-of-the-art monolingual and multilingual models on these new benchmarks, opening up avenues for research on sentiment analysis and emotion detection in different languages.
The MULTI-SCALE Dataset We translate two English scalar adjective datasets into French, Spanish and Greek: DEMELO consists of 87 hand crafted half-scales 3  (de Melo and Bansal, 2013) and WILKINSON contains 12 full scales  (Wilkinson and Oates, 2016). We use the partitioning of WILKINSON into 21 half-scales proposed by  Cocos et al. (2018). In what follows, we use the term "scale" to refer to half-scales. The two translators have (near-)native proficiency in each language. They were shown the adjectives in the context of a scale. This context narrows down the possible translations for polysemous adjectives to the ones that express the meaning described inside the scale. For example, the Spanish translations proposed for the adjective hot in the scales {warm < hot} and {flavorful < zesty < hot || spicy} are caliente and picante, respectively.
Additionally, the translators were instructed to preserve the number of words in the original scales when possible. In some cases, however, they proposed alternative translations for English words, or none if an adequate translation could not be found. As a result, the translated datasets have a different number of words and ties. Table  1  shows examples of original English scales and their French, Spanish and Greek translations. Table  2  contains statistics on the composition of the translated datasets. In order to test contextual models on the ranking task, we collect sentences containing the adjectives from OSCAR  (Suárez et al. , 2019)   modify. In order to keep the method resource-light, we gather sentences that contain the adjectives in their unmarked form. For each scale s, we randomly select ten sentences from OSCAR where adjectives from s occur.
The SCAL-REL Dataset SCAL-REL contains scalar adjectives from the DEMELO, WILKINSON and CROWD  (Cocos et al. , 2018)  datasets (i. e. 79 additional half-scales compared to MULTI-SCALE). We use all unique scalar adjectives in the datasets (443 in total), and subsample the same number of relational adjectives, which are labelled with the pertainym relationship in WordNet  (Fellbaum, 1998). There are 4,316 unique such adjectives in WordNet, including many rare or highly technical terms (e. g. , birefringent, anaphylactic). 4 Scalar adjectives in our datasets are much more frequent than these relational adjectives; their average frequency in Google Ngrams  (Brants and Franz, 2006 ) is 27M and 1. 6M, respectively. We balance the relational adjectives set by frequency, by subsampling 222 frequent and 221 rare adjectives.