dataesr/scientific-paragraphs-categorization huggingface dataset:
Corpus data We worked on the Frantext corpus  [22] , which in 2016 contained 4674 texts and 232 millions of words for the chosen time range. More details are given in Appendix C 2. It would have been tempting to make use of the large database Google Ngram, yet it was not deemed appropriate for our study, as we explain in Appendix C 3. We studied changes in frequency of use for about 400 instances of semantic expansion processes in French, on a time range going from 1321 up to nowadays. See Appendix C 4 for a complete list of the studied forms. Extracting patterns from corpus data a. Measuring frequencies We divided our corpus into 70 decades. Then, for each form, we recorded the number of occurrences per decade, dividing this number by the total number of occurrences in the database for that decade.
It is also worth noticing that the model predicts that the Inverse Gaussian would be suited for both the growth and the latency, while the other candidates are appropriate for only one of these quantities (the growth time for the Poisson distribution, the latency time for the exponential). From the empirical procedure, we can also extract, for both the corpus and numerical datasets, the statistical distributions of the slopes of the logit transform of the sigmoidal part. Corpus data (Fig. 13 ) is best fitted by the Inverse Gaussian, by comparison with a Maxwellian and a Gaussian. Statistical tests favor consistently the Inverse Gaussian (Table  III ). All these three fits have been done without optimization, using the mean and the variance of the data to compute the parameters accordingly.
We showed that this process of cognitive actuation is sufficient to explain the S-curve. In a sense, the cognitive interpretation is more economic, as it explains the S-curve (and the latency) by the mechanism of actuation alone, instead of positing a prerequisite actuation, and then explaining the S-curve (but not the latency) as social propagation, which is the case in the sociolinguistic framework. Occam's razor inclines therefore towards the cognitive interpretation of our model and of language change in general. the twentieth century: Indeed, the number of texts by decade roughly follows an exponential increase (Fig. 18 ). Frantext, while being much smaller than Google Ngram, provides much cleaner and more controlled results (see C 3). We decided to start from the decade 1321-1330, as from this date all decades are associated with at least seven texts.
This would not be a problem if the proportion of dramas were more or less constant across the decades, which is not the case. This problem vanishes in more recent times, when represented orality appears also frequently in novels, while drama becomes itself more sophisticated and shifts further away from daily language. Frantext is not only a database. It comes also with built-in text-mining algorithms which allow to submit very refined queries to the database. Such queries can make use of booleans and a given number of blank words. For instance, the query (à|a) &q(1,2) (insçu|insu|insceu) (&q(1,2) is a blank slot for any one or two words) will retrieve occurrences such as à l'insu, à leur insu, but also à son propre insu. This kind of flexible requests are especially relevant when one is looking for specific constructions with a filling slot, as the corresponding possibilities cannot be exhaustively predicted.
This kind of elaborations prove to be all the more useful in the first stages of the evolution, where a functional construction has not yet become entrenched into an idiomatic form and can still be found in a large diversity of variants. Once a request is submitted to the database, Frantext returns a datafile whose contents may vary according to the needs of the user. Depending on the options one chooses, the file displays, for each text, the text reference, the publication year, and the total number of occurrences of the query in that text. Next to this automatized procedure, we can also look across all individual occurrences in their context, as a sanity check. This was used frequently to help refining our queries. Unfortunately, it was impossible to ask Frantext for a file providing the statistics of the corpus itself, listing the number of occurrences per text reference.
Alas, finding appropriate corpora covering a long time period in a given language is not obvious. As discussed in C 3, Google Ngram cannot be used for texts earlier than the nineteenth centuries, since the scanning procedure does not lead to reliable digital data. For the English language, the reputed British National Corpus restricts itself to the twentieth century. The Helsinki Corpus spans a time period suited for our purposes, but the texts are too sparse (450 in total) for the corpus to be fitted for a statistical survey. The CORDE corpus, in Spanish, spans several centuries (XIII th to XX th ), and gathers an impressive amount of data as well (250 M words), but it covers different variants of Spanish (Argentinian, Colombian, Castillan, etc. ) which cannot be blended together when it comes to investigate semantic expansions (note that CORDE dutifully offers to treat them apart, but then the database is not extensive enough for each of the variant separately).
The querying system also suffers from serious limitations, and it is not possible to submit complex queries as is the case with Frantext. This latter database is therefore truly remarkable in many aspects and has to be considered an exception. We thus leave to further studies the case of other languages. A last remark is in order: We deliberately do not provide any translation of the studied forms (C 4), however obscure they may appear to the reader. Indeed, these forms have all undergone a semantic expansion, so that a translation would be most mistaking as it would concern only one among several meanings adopted by the form. The only satisfying way of glossing the items we studied would have been to find forms which not only have the same meaning, but have also undergone (at least roughly) the same meaning shifts, as in the case of anyway and de toute façon for the later stages of their respective semantic evolutions.
Why not using Google Ngram? Google Ngram (https:/books. google. com/ngrams) gathers an impressive quantity of digitalized books from about the sixteenth century. It hosts about 800,000 texts in French (about two hundred times more than Frantext). Nevertheless, it presents some major limitations which make this database inappropriate for the present study, as we discuss in this section. Some biases of the Google Ngram database have already been stressed in the recent past  [54]. However, these concerns are specifically relevant for lexical changes, most subject to socio-historical contingencies, and they do not straightforwardly apply to our aims. Functional words, unlike proper names like 'Frodo' or items like 'computer', are not that sensitive to cultural shifts. However, there are other serious limitations, more crucially relevant for our study, that we point out here.
In the following, we use Google Books (https://books. google. com/) as a probe to the contents of Google Ngram, though the two algorithms are different (e. g. the former does not regognize ponctuation while the latter does), and the exact overlapping between the contents of the two databases is unknown. The first concern about Google Ngram regards quality of digitalization. Texts older than the nineteenth century have been printed in fonts for which the character recognition algorithm has clearly not been optimized. For instance, the following sentence from The royal dictionary abridged, in two parts, by Abel Boyer, 1715: 'Parler avantageusement de quelqu'un, to speak well of one, to speak much to his advantage, to give a good character of him, to speak honourably of him. ' has been transcribed as: 'Parler avantageusement e quelqu'un, 1 • speak well of one, te steak much to his advant 1ge, to ive a gead characier of him, to steak h2nourably of him.
By comparison, every text in the Frantext database has been digitalized with great care and such blatant errors are not to be found. The second point is the kind of available data. Google Ngram provides statistics on ngrams, which are strings of n successive items (the so-called 'grams'), with n ranging from 1 to 5. For each n-gram, it is provided, per year, the number of times it appears and in how many texts. Thus one cannot identify in which texts it appears most; nor can one have access to its context of use. The only way to probe the contents of Google Ngram is through Google Books (which we used here for all the discussed examples), yet it seems impossible to know the exact overlap between the two databases. This data structure based on ngrams is furthermore limiting when it comes to slot constructions.
For instance, the French construction 'à X reprises', with X being a quantity, can hardly be tracked using Google Ngram, as it corresponds to far too many n-grams, which would need to be listed one by one: 'à deux reprises', 'à deux ou trois reprises', 'à plusieurs reprises', 'à de nombreuses reprises', etc. This search is made all the more difficult by the fact that 'à' did not always take an accent in older texts. In contrast, with Frantext, as we have seen in C 2, one can work out an elaborate request using booleans and blank words to capture the diverse uses of this construction and overcome the orthographic difficulties. The third and final point we want to stress here is the choice of texts and their dating. In Frantext, a text may appear in several editions, as is the case for Le Cid, by Pierre Corneille, which appear thrice in the database, associated to the years 1637, 1637 and 1682.
These dates usually correspond to the first edition of a book, rather than to the edition which is actually digitalized (such information being also provided). Google Ngram displays about thirty versions of Le Cid, with publication ranging from 1775 to 2013, some of them being ascribed to Jean Racine (as they are found in several editions of a book called Oeuvres de J. Racine et de P. et T. Corneille). The case of Le Cid is, in Frantext, quite an exception, while in Google Ngram, most famous classical novels from past centuries are found in a dozen versions at least. The contents of the database is problematic as well. As highlighted in  [54] , Google Ngram over-represent academic literature. This also tends to bias the data. For instance, among the fourteen results of the request 'par ma barbe' on the French Google Books subdatabase, for the years 1950-2000, only three of them are relevant, two being modern translations of older texts (Don Quixote and a nineteenth century German play by Töpffer).
The third one comes from an anthology of French folktales. All other occurrences are academic quotes and glosses of past works, or reprints of such works. In such a case, it means than only one fifth of the occurrences would be reliable as a reflect of language use in this time period (two of them being borderline cases). Frantext, on the other hand, has two occurrences of 'par ma barbe', one of them from the song lyrics of singer Georges Brassens, the other from a 1988 translation of a Shakespeare play (and so more debatable). There is thus almost as many relevant occurrences in Frantext and Google Ngram (two versus three), while none in Frantext are completely irrelevant. This being said, Google Ngram is a formidable tool, which can lead to interesting insights and be of great use. It is not, however, fitted for the work that we performed, where we need an accuracy and a reliability that this database is unable to provide.
Studied forms Making use of the study of Frantext database and its retrieving tools, we looked at the frequency of use of about 400 hundred semantic expansions in the functional realm (with the exception of liberté and some lexical constructions such as á court terme, which we have shown to suggest the further generality of the pattern). We selected these forms according to several criteria: They must have undergone at least one semantic expansion towards a functional use during the time period under consideration; they must be easily distinguished from compositional uses (e. g. entre deux, in the meaning of 'in between', can be confused with occurrences of literal meaning 'between two'). The set of chosen forms is far from exhausting the pool of possible examples. On the table below, we provide the full list of studied forms.
'BUG' corresponds to a flaw of Frantext, sometimes unable to build up the output file of the query. This bug cannot be overriden through a manual manoeuvre, for it is caused by a faulty encoding of some parts of the texts. The data thus exist, but could not be retrieved. An upper-case 'NO' indicates that no such pattern has been found in the time-evolution of the frequency of that form. The fact that a form does not follow an S-curve during its semantic expansion may spread doubt on the genericity of this pattern. In many cases however, the pattern was rejected because the data was too spurious, but its overall behavior would not be incompatible with an S-curve. It is nonetheless interesting to note that the robustness of the pattern does not depend excessively on the scarcity of data. Indeed, instances associated to a very low number of occurrences can lead to a very clean pattern (e.
Environmental Monitoring of Source Water Mesocosms were set up at Palmer Station, Antarctica, using seawater from the station's intake, located at a depth of 6 m, 16 m from the shore of the station. This source water was monitored during the experimental period (August-December 2013) to determine any changes in environmental parameters that might influence bacterial community responses in mesocosm experiments. Samples for dissolved nutrients (phosphate, silicate, nitrite, and nitrate) and particulate organic carbon and nitrogen (POC and PN) were processed according to Palmer LTER standard protocols 1. Briefly, nutrient samples were filtered through combusted 0. 7-µm glass fiber filters (Whatman, GE Healthcare Life Sciences, Piscataway, NJ, United States) and frozen at -80 • C until analysis on a SEAL AutoAnalyzer 3 (data available at doi: 10.
6073/pasta/e893d71c5586769731875d49fde21b1d;  Ducklow et al. , 2016). Samples for DOC were filtered through combusted glass fiber filters and stored frozen until analyzed by hightemperature catalytic oxidation in a Shimadzu TOC-V, following previously described methods  (Carlson et al. , 2010). POC and PN samples were collected on combusted 0. 7-µm glass fiber filters from 1 to 3 L seawater and were frozen at -80 • C until analysis via combustion using a Perkin Elmer 2400 Series II CHNS/O Analyzer. Seawater intake chl a data are routinely collected through the LTER project (data available at doi: 10. 6073/pasta/c2df461937789b5e53019dadcd29fc57;  Schofield and Vernet, 2016). Bacterial production and abundance were determined as described below.
Data Analysis To separate the effects of incubation day in an experiment (i. e. , days into incubation period), DOM+ treatment or control, and any differences between experiments on univariate bacterial (i. e. , cell abundance or production) parameters, we performed ANOVA on nested linear models using the lm and anova functions in R  (Chambers, 1992; Venables and Ripley, 2002; R Development Core Team, 2008). Two linear models are nested if one (the restricted model) is obtained from the other (the full model) by removing a term from the full model. An ANOVA performed on such nested models provides the equivalent of the F test for goodness of fit and indicates whether individual terms improve the model. This provided a statistical test of our hypothesis as the season progressed. The number of OTUs observed in each library, Shannon's diversity, and Pielou's evenness, were calculated using the BiodiversityR package in R  (Kindt and Coe, 2005).
Differences in overall bacterial community composition between samples were visualized with non-metric multidimensional scaling (NMDS) based on Bray-Curtis similarity using the metaMDS function in the vegan R package  (Oksanen et al. , 2015). Co-occurrence network analyses based on Pearson's correlations were used to examine relationships among OTUs and external variables (i. e. , experimental treatments and bacterial production). Networks were generated using the Cytoscape CoNet plugin version 1. 1. 1. beta and were visualized in Cytoscape version 3. 4. 0  (Shannon et al. , 2003; Faust et al. , 2012). The R code used for all data analysis and figure production can be accessed at https: //github. com/cmluria/DOM.
ACKNOWLEDGMENTS We acknowledge the help of the following people for providing field and laboratory assistance at  Palmer Station  as part of this project:  Jamie Collins ,  Sharon Grim ,  Sean O'Neill ,  Monica Stegman ,  Sebastian Vivancos , and  Madelyne Willis , as well as the staff at  Palmer Station. We thank  Annaliese Jones ,  Leslie Murphy , and  Naomi Shelton  for conducting laboratory analyses and  Doug Rasher  for helpful discussion.
FUNDING CL was partially funded by the  Graduate School  and the  Department of Ecology and Evolutionary Biology at Brown University  and the  Brown University-Marine Biological Laboratory   Joint Graduate Program. This material is based upon work supported by the  National Science Foundation  under Grant Nos. ANT-1142114  to  LA-Z ,  OPP-0823101  and  PLR-1440435  to HD, and  ANT-1141993  to JR. The  Gordon and Betty Moore Foundation  grant 1711 supported work by DR.
RESULTS Study area. Our previous work used 16S rRNA gene amplicon data and quantitative PCR (qPCR) to examine correlations between whole microbial communities, nutrients, and DO across the geographic range of the 2013 seasonal hypoxia  (21). Here we selected six of those samples from offshore of the region between Atchafalaya Bay and Terrebonne Bay (D=, D, and E transects). These sites ranged considerably in DO concentration (ϳ2. 2 to 132 mol • kg Ϫ1 ), and we chose them to facilitate a detailed investigation of the metabolic repertoire of individual taxa across the span of suboxic (1 to 20 mol • kg Ϫ1 DO) to oxic (Ͼ90 mol • kg Ϫ1 DO) (5) water. Microbial samples from these sites were collected at the oxygen minimum near the bottom. Site depth ranged from 8 to 30 m, with the hypoxic (Ͻ2 mg • liter Ϫ1 /62.
com/publications). We also found numerous outer membrane transporters (including cation symporters), outer membrane receptors (OMR) (TonB dependent) (which play important roles in transport of metals, vitamins, colicins, and other compounds), and outer membrane factors (OMF). Most genomes also had large numbers of duplicated genes (24 in Bin 45-2), identified via hidden Markov model searches against the Sifted Families (SFam) database  (41) , annotated as "Por secretion system C-terminal sorting domain-containing protein," some of which were associated with GH16. These genes likely play a role in sorting C-terminal tags of proteins targeted for secretion via the Por system, which is essential for gliding motility and chitinase secretion in some Bacteroidetes  (42). The extensive gene duplication may indicate expanded and/or specialized sorting functionality and suggests an emphasis on protein secretion in this group.
The data are available at the NCBI SRA repository under the BioSample accession numbers SAMN05791315 to SAMN05791320 (DNA) and SAMN05791321 to SAMN05791326 (RNA). DNA sequencing resulted in a total of 416,924,120 reads that were quality trimmed to 413,094,662 reads after adaptors were removed using Scythe (https://github. com/vsbuffalo/scythe), and low-quality reads (Q Ͻ 30) were trimmed with Sickle (https://github. com/ najoshi/sickle). Reads with three or more N's or with an average quality score of less than Q20 and a length of Ͻ50 bp were removed. Metagenomic reads from all six samples were pooled, assembled, and binned using previously described methods  (77, 78). Briefly, quality-filtered reads were assembled with IDBA-UD (79) on a 1TB RAM, 40-core node at the Louisiana State University high-performance computing Decoding Microbes of the Dead Zone cluster SuperMikeII, using the following settings: -mink 65 -maxk 105 -step 10 -pre_correction -seed_kmer 55.
S2 ) or via 16S rRNA gene sequences, when available. 16S rRNA genes were identified via CheckM, and these sequences were aligned against the NCBI nr database using BLASTN to corroborate CheckM assignments. In the case of the SAR202 genomes, which did not have representative genomes in either the ribosomal protein tree or the CheckM tree, the 16S rRNA gene sequences for two of the three bins (43-1 and 43-2) were available and aligned with the sequences used to define the SAR202 clade (24) (Fig. S5 ). Alignment, culling, and inference were completed with MUSCLE (83), Gblocks  (87) , and FastTree2 (88), respectively, with the FT_pipe script. The script is provided in the supplemental information at http://thethrashlab. com/publications. The 16S rRNA gene tree for subclade assignment of SAR406 (Fig.
S4 ) was assembled by subjecting the four 16S sequences predicted by CheckM to a BLAST search against a local GenBank nucleotide database using blastn (v. 2. 2. 28ϩ)  (89) , selecting the top 100 nonredundant hits to each sequence, and manually removing all hits to genome sequences. These sequences were combined with previously defined SAR406 subclade reference sequences  (26) , fosmid 16S sequences  (27) , single-cell genome sequences  (23) , and run through alignment, culling, and inference with FT_pipe. Taxa with identical alignments were removed with RAxML v 8. 2. 8 (85) using default settings, and the final tree was inferred using FastTree2  (88). For putative candidate phylum (CP) genomes, taxonomy was also evaluated by examining the taxonomic identification for each of the predicted protein sequences after a BLASTP search against the NCBI nr database.
After the BLAST search, the number of assignments to the dominant one or two taxonomic names, along with the number of assignments to "uncultured bacterium," was plotted for each genome according to the bit score quartile (Fig. S3 ). Quartiles were determined in R using the summary function. Bin 56 has two ribosomal protein operons on scaffold_2719/Ga0113622_1153 and scaffold_21777/Ga0113622_1009. In the ribosomal protein tree, the former placed the organism in the Planctomycetaceae, while the latter (which was much smaller) placed the organism in CP WS3. The majority of BLASTP annotations to the nr database matched Planctomycetaceae taxa, as did the 16S rRNA gene sequences found in the genome, so the Bin 56 organism was designated a Planctomycetes and not WS3 and excluded from this study.
The 16S rRNA gene from Bin 50 was also used to infer taxonomic identity using an established phylogeny for the WS3 clade  (68)  and relevant outgroups. The Bin 50 sequence was subjected to a BLAST search against the GreenGenes database (December 2013) with megablast, and since many of the top hits belonged to the PAUC34f clade, these were included with the sequences from Farag et al. (68). Alignment, culling, and inference were completed with FT_pipe. Node labels were constructed with the newick utilities (90) script nw_rename. Metabolic reconstruction. After binning, genomes were submitted individually to IMG (Integrated Microbial Genomes) (91) for annotation. Genome accession numbers are in Table  1 , and all are publically available. Metabolic reconstruction found in Fig. S5  to S7 and in Table  S1  and Fig.