dataesr/scientific-paragraphs-categorization huggingface dataset:
ASAP then scores all new groups with a probability of panmixia. It also scores the new partition using an ad-hoc score computed from both the barcode gap width and probabilities of panmixia. After the group(s) and partition scoring, ASAP then continues the clustering by looking after the next distances until another partition is built. The algorithm stops when all sequences are merged into a single final group. iii) Computing p-values a. For each group: we aim at computing a p-value for a newly created group that is a merge of two or more subgroups. We compute Π intra the average pairwise distance between sequences within the subgroups and Π inter the average pairwise distance among sequences of different subgroups (Figure  1 ). We then compare Π intra to its theoretical distribution, computed by Monte-Carlo simulations of a neutral coalescent model assuming a single panmictic species with a sample size m and a coalescent mutation rate θ = Π inter / [2×(1-1/m)].
iv) Recursive splits Once a new partition is built, ASAP tests for each of the groups of the partition whether its pvalue is lower than a given risk (by default 1%) and consequently should be split. When a group is split, ASAP recursively descends to all its subgroups and assesses whether they should be split as well. v) Relative barcode gap width ASAP also computes a relative barcode gap width associated to the current partition (Supplementary Material 1). The partition is associated to a threshold distance d T that is the mid-point between the current distance, d C (with rank r C ), that triggered the merging and the previous distance in the list d C-1 (with rank r C -1). A barcode gap corresponds to a "jump" in the distance values in only few ranks. While increasing only few ranks in the list, the distance will "jump" from a value that is (much) less than d T to a value that is (much) higher than d T.
To quantify the barcode gap width, ASAP scans downward the distance list from d C-1 until it finds the first distance smaller than 0. 9d C-1 : this is d L which rank is r L in the list. It then scans from d C the distance list upward until it finds the first distance above 1. 1d C : this is d H which rank is r H. The relative gap width W is defined as: W = [ (d H -d L ) / (d H +d L +1) ] / (r H -r L ). We normalized the difference of distance (d H -d L ) by (d H +d L +1) to compute the "relative" width of the gap; the "+1" only prevents the ratio to be very high when distance values are very small. The higher the W, the larger the barcode gap.
Tests on empirical data To compare the results obtained by four methods (ASAP, (m)PTP, (m)GMYC and ABGD), we selected 10 empirical COI datasets covering various taxa (birds, mammals, amphibians, insects, crustaceans and molluscs) and including 44 to 2,574 specimens that belong to 5 to 643 species (Table  1 )  (Borisenko, Lim, Ivanova, Hanner, & Hebert, 2008; Elias-Gutierrez, Jeronimo, Ivanova, Valdez-Moreno, & Hebert, 2008; Hajibabaei, Janzen, Burns, Hallwachs, & Hebert, 2006; Kerr et al. , 2007; Puillandre, Cruaud, & Kantor, 2010; Puillandre, Baylac, Boisselier-Dubayle, Cruaud, & Samadi, 2009; Puillandre, Fedosov, Zaharias, Aznar-Cormano, & Kantor, 2017; Puillandre et al. , 2011 Puillandre et al. , , 2012;; Smith, Poyarkov Jr. , & Hebert, 2008). Among them, five correspond to datasets published by one of the authors to facilitate the interpretations of the results.
An eleventh dataset, including 9,396 sequences of moths (publicly available from BOLD), was used to estimate and compare the computation times of ABGD and ASAP. A dataset of this size could not be analyzed by (m)GMYC or (m)PTP as the phylogenetic reconstruction is too costly. For all empirical datasets, we used the web version of ABGD, with default parameters. Only the initial partitions were considered, and only the more stable partition(s) (i. e. the partition(s) found with several P in the vicinity of the barcode gap) was (were) reported. For ASAP, we used a recursive split probability of 0. 01 (see iv), and report a) the partition with the best asap-score as well as b) the partition that is closest to the "correct" one among the two best partitions, according to their asap-scores. For GMYC and mGMYC, ultrametric trees were reconstructed using BEAST 2  (Bouckaert et al.
, 2014) , with an independent GTR substitution model for each codon position. Relative divergence times were estimated using a relaxed lognormal clock with a coalescent prior and a constant population size, following the recommendations of  Monaghan et al. (2009). The number of MCMC steps were 20M (Gemmuloborsonia, Benthomangelia, Lophiotoma and Eumunida datasets), 100M (Amphibians, Cladocera, Mammals, Sphingidae and Turridae datasets) and 200M (Birds dataset), sampled every 2,000, 10,000 and 20,000 steps respectively. Convergence of the runs was assessed using TRACER 1. 6  (Rambaut & Drummond, 2014)  to check that all effective sample size values exceeded 200. Consensus trees were calculated after discarding the first 25% of the trees as burn-in, with the option "Common Ancestry" for node height.
Empirical datasets We first assessed the ability of ASAP through a proxy that is its ability to retrieve the "correct" number of species in 10 empirical datasets (Table  1 ). The datasets were selected to represent test cases of different sizes (from 44 sequences/5 species to 2,574 sequences/643 species). We first report the number of species predicted in the partition with the best asapscore (ASAP 1 st ): we found that in 4/10 of the datasets, the partition with the best asap-score is very close to the reference one (less than 5% difference in terms of species numbers) and that 8/10 is close (less than 10% difference). If we also consider the partition with the second best asap-score (ASAP 1 st and 2 nd ), the degree of accuracy increases to 6/10 for the very close ones and 9/10 for the close ones.
This is a good indication that ASAP users should consider not only the partition with the best asap-score but also few subsequent ones. It is important to report that here no extra biological knowledge was considered for ASAP predictions. One could for example use threshold distances (e. g. d T or d C ) to prefer one partition over another despite a poorer asap-score (e. g. in most clades intra-specific diversity is typically on the order of 1%, not on the order of 10%). Obviously, other criteria and characters should also be used to choose a final species partition, in an integrative taxonomy context. One of the ASAP main qualities is that it is extremely fast compared to any method that relies on tree reconstruction. The online version takes 45 seconds for the largest dataset of Table  1  (2,574 aligned sequences; 643 species) for all steps of the complete method: mainly creating the distance matrix, performing the clustering and computing probabilities by Monte Carlo at each node.
We observed that the CPU time increases linearly with the number of species in the datasets (Figure  2 ) and only to a lesser extent with the number of sequences (data not shown). We estimate the CPU cost at 0. 07 sec per species for the current web version. This suggests that most of the CPU time is taken by probability estimations of significant nodes (see method, section iii) (non-significant ones are not as costly in our implementation as we increase the number of replicates only for nodes with low probabilities). The number of significant nodes likely increases approximately linearly with the number of species. The time for distance matrix computation and clustering both increase quadratically with the number of sequences and are independent from the number of species. On a curated unpublished moth dataset, it took 6 min 35 on the website to delimit 2,466 species (best asap-score) or 2,067 (second best asap-score) from 9,396 sequences.
Subsequent partitions with lower asap-scores are close to one or the other of these two first partitions. Because of its rapidity, ASAP web server accepts up to 10 4 sequences (unlike the ABGD server). We also took the opportunity of analyzing the 10 datasets to assess the performance of other methods: ABGD which is solely based on pairwise distances, PTP and mPTP that were run on an ML trees (i. e. RaxML) and GMYC and mGMYC on an ultrametric trees estimated by a Bayesian MCMC method (i. e. BEAST). Results (Table  1 ) show that ABGD performance is similar to ASAP 1 st -2 nd , that PTP and mPTP tend to not perform very well, that GMYC performs very well provided that the number of species is not too large and that, as previously reported in the literature, mGMYC generally oversplits  (Fujisawa & Barraclough, 2013; Kekkonen & Hebert, 2014).
Simulated datasets We then assess the theoretical performance of ASAP using Monte-Carlo simulations of a multispecies coalescent framework. In brief, a random species tree is generated using either a Radiation model, where all species arose in single event, or a Yule model, where the speciation events occur at constant rate independently in all branches. In both model, we tune the separation of time scales (speciation versus intra-specific coalescent events) using a speciation rate that is expressed in coalescent time (i. e. N generations per unit of time). The lower the speciation rate, the better the separation of time scales. For example, when the speciation rate is 0. 1, speciation events are 10 times slower than pairwise coalescent events within species.
DISCUSSION We introduced a new species delimitation program, ASAP, fully exploratory, in the sense that it does not require any a priori knowledge, neither on the number of species, the species composition, or any biological information, such as a phylogenetic tree or a priori-defined intraspecific genetic distances. Only pairwise genetic distances are used to build a list of partitions ranked by a score. This composite score is computed using the probabilities of groups to be panmictic species and the barcode gap widths. ASAP overcomes the two mains limitations of ABGD, namely (i) the need for an a priori defined P and (ii) the lack of a scoring system. However, and contrary to some other methods, ASAP still outputs several partitions, ranked by their asap-scores. A list of the "best" partitions (10 by default) is provided in the output together with their gap-width score, their p-value, their threshold distance d T and the number of species they correspond to.
When a partition is selected by a click in any of the three panels, it is automatically highlighted in the two other components. We also propose a complementary representation, where we display the hierarchical tree with, at its leaves, the 10 best ASAP partitions where their groups are depicted as boxes (that are similar to the boxes of Figure  1 ). We have evaluated ASAP strengths and weaknesses using both real and simulated data. Our benchmark shows that ASAP performs well delivering partitions in a matter of minutes even for datasets as large as 10 4 sequences. ASAP is thus meant to be applied on large single-locus datasets when no species hypothesis is available, as typically produced in DNA-barcoding projects. Although the web version limits the input to 10 4 sequences, more sequences can be analyzed using a local command-line version of ASAP (sources are available on the webserver).
This additional step in the (m)GMYC and (m)PTP methods potentially introduces a bias, because a) phylogenetic trees reconstructed on a single locus may differ drastically from the species tree, and b) the limited number of sites in a single marker may lead to incorrectly reconstructed trees. Consequently, (m)GMYC and (m)PTP have been shown to be sensitive to the reconstruction method  (Tang, Humphreys, Fontaneto, & Barraclough, 2014). On the contrary, it could be argued that relying only on genetic distances, i. e. without testing if these differences actually correspond to distinct evolutionary histories, and not to homoplasy, must be used with caution. Indeed, the efficiency of each method in delimiting species probably depends on various characteristics of the species and datasets (number of samples, number of species, population sizes…), and applying several methods to a given dataset is a strategy commonly applied to maximize the probability to detect species complexes, identified as groups of species whose limits vary depending on the method.
Importantly, several other methods can also be used to delimit species, such as BINs  (Ratnasingham & Hebert, 2013) , Jmotu  (Jones, Ghoorah, & Blaxter, 2011)  or VSEARCH  (Rognes, Flouri, Nichols, Quince, & Mahé, 2016) , among others (e. g. Rannala & Yang, 2020). We are also aware that the number of predicted species is only a proxy to assess the performance of the different methods. Indeed, other metrics such as the F-measure  (Larsen & Aone, 1999)  or the number of splits or merges  (Ratnasingham & Hebert, 2013)  give also insightful information. Some of them are even implemented in meta-analysis software such as LIMES  (Ducasse, Ung, Lecointre, & Miralles, 2020) , which could be used to perform a more extensive benchmark of all existing methods using a wider spectrum of metrics. More generally, and as advocated by the proponents of the integrative approach in taxonomy, the use of a single marker with a single method of species delimitation should be avoided, precisely because each method has its own limitations.
ACKNOWLEDGMENTS This work was supported by the  CONOTAX  project funded by the  French National Research Agency  (grant number  ANR-13-JSV7-0013-01 ). The authors would like to thank  Amaury Lambert  for his valuable advices,  Malcolm Sanders ,  Paul Zaharias ,  Laure Corbari  and  Romain Sabroux  for having tested preliminary versions of ASAP, and all the users of ABGD.
An illustration of the clustering algorithm on a small dataset of nine sequences. On the lower part, we report how ASAP proceeds (downward in the figure) through the list of ranked distances (on the left), merging successively sequences into groups (highlighted in colored blocks). For each new group, ASAP computes a p-value that this new group is a panmictic species (values reported on the right part) based on pairwise differences within (intra) and between (inter) subgroups. Furthermore, each time a new group is created, a new partition is built (a sequence of blocks in the central part) that is associated to the current distance d C. The distances d C at which the partitions are instantiated are represented in a phenetic tree (top part). Each node is a group, each horizontal dashed line is a partition.
Distribution of AAI values calculated from type strain comparisons was found to be multimodal, which led us to propose a re-organization based on average amino acid identity (AAI) that separates the Chryseobacterium genus into four different genera. These correspond to the multiple lineages apparent in previous publications  [18]  and also recognised as separate genera in the Genome Taxonomy Database  [40]. This aligns genus assignments so that the type strain for each species in each genus has ≥76 % AAI values compared with the type strain of the type species of its genus. Twenty-eight strains whose genome sequence has not previously been reported are listed in Table  1. This includes the type strains of Chryseobacterium lactis, Chryseobacterium carnis, Chryseobacterium bernardetii and Chryseobacterium nakagawai that had been described in the earlier study  [12] , along with one or two additional strains from the same DNA-DNA hybridization group.
There remain more than 30 species of the genus Chryseobacterium with validly published names that have no type strain genome sequenced. DNA-DNA hybridization was performed as described previously  [41]. All strains were biochemically characterised in all or most of a range of 68 conventional biochemical tests by methods described previously  [42]  and wholegenome sequences were generated as described previously  [32, [43] [44] [45] [46] [47]. Sequence alignments were performed using the ClustalW module in BioEdit v7. 0. 5. 3  [48]. For analysis of 16S rRNA genes, sequences were trimmed to match the start and end of the near full-length sequence JX100817 (deposited as Chryseobacterium carnis strain G0081 T ). All pairwise comparisons with a 16S rRNA gene sequence identity greater than >98. 65 % were considered as potentially representing the same species, as this limit has been recommended for species demarcation  [49].
The rpoB gene sequences were extracted from WGS data as described previously  [32]. Phylogenetic trees were reconstructed using MEGAX  [50]  with the Jukes-Cantor substitution model. Proteomes from each genome were generated by Prodigal v2. 6. 2  [51]. For each pairwise comparison, an all-versus-all search of all proteins was carried out using BLASTp v2. 4. 0+  [52]  in both directions. If both directions of BLASTp searches resulted in the same protein match (pair) and exceeded 40 % in amino acid identity and 50 % in coverage length, we included the protein sequences for computing the arithmetic mean amino acid sequence identity (AAI). Percentage of conserved proteins (POCP) scores were calculated as previously described  [53]. GGDC formula 2 was used to generate the predicted DDH values. The Jspecies software package version 1.
2. 1 was used to calculate average nucleotide identity with BLASTn alignments (ANIb)  [31, 34]. Additional data visualizations were produced using JMP v11 (SAS Institute). The gene pairings identified during the AAI pairwise comparisons described above were used to generate a list of 488 loci present in a single copy in all of the strains listed in Table  S1. The maximum sequence length for each orthologous group was computed, and groups with members having a length ≥90 % of this maximum were selected for alignment. These 148 were aligned (as protein sequences) using ClustalW in BioEdit v7. 0. 5. 3  [48] , and manual refinements were made to align start and stop codons. Unusually divergent (<25 % nucleotide identity) loci were filtered out, and the remaining 119 gene sequences from each genome were concatenated.
Long-read (PacBio) sequencing was done on all four strains. ANIb comparisons using these assemblies confirms that strains G0207, G0239, and H5143 represent a single species, and differ from G0201 and any other species whose genome has been sequenced thus far, which would be expected as the type strain of Chryseobacterium shandongense has not yet been sequenced. There are phenotypic differences between the type strain of Chryseobacterium shandongense and the sequenced '132 group' strains G0207, G0239, and H5143; the latter all grow on MacConkey agar, and none are mucoid. The species description  [86]  is emended to include the phenotypes of Chryseobacterium shandongense strains from our '132 group'. The '224 group' , represented by strain F5649, was previously attributed to Chryseobacterium hominis on the basis of 16S rRNA gene sequence analysis and DNA-DNA hybridization data  [12].
Its 16S rRNA sequence is most similar to that of the type strain of Chryseobacterium rhizoplanae JM-534 T , but ANIb comparisons between this strain's genome and those of assemblies of two Chryseobacterium rhizoplanae SRA data sets (SRX3054699 and SRX3054698) were in the range of 81-82 % (data not shown). Thus G0162 is currently the only known representative of its species. The description of Chryseobacterium nakagawai is emended below to reflect removal of strain G0162 from the species. Strain G0186 was the representative strain for the '123 group' , along with strain G0240 and was identified as Chryseobacterium ureilyticum on the basis of a 16S rRNA gene sequence identity of 99. 2 %  [12]. However, its ANIb compared with Chryseobacterium ureilyticum strain DSM 18017 T was less than 83 % and it did not match any other species.
nov. Planobacterium taklimakanense is a rare example of an enigmatic genus assignment -we had to consider whether or not to classify it as a member of the genus Kaistella. The AAI between Planobacterium taklimakanense NCTC 13490 T and CCUG 49689 T , the type strain of Kaistella koreensis (the type species of the genus Kaistella) was intermediate at 75. 1 %. The highest AAI that the type strain of Planobacterium taklimakanense, NCTC 13490 T , shared with any other type strain was 76. 25 % (Kaistella haifensis), and it had an AAI >76 % when compared to three of the type strains of species of the genus Kaistella, but it also had an AAI < 74 % when compared with the type strains of nine other species of the genus Kaistella. Several characteristics are shared among members of the proposed genus Kaistella, such as small genome size, presence of a carotenoid biosynthetic gene cluster (accessions numbers SNV33133-33186) including lycopene cyclase (SNV33175), absence of the darA gene (Accession number EFK37140 in Chryseobacterium gleum) and a corresponding absence of the flexirubin pigments that its encoded protein would produce, and a fatty acid composition with >10 % C 15 : 0 anteiso.
However, unlike the strains belonging to the genus Kaistella, Planobacterium taklimakanense lacks the nosZ gene (accession number KMQ71079 in Kaistella koreensis) and associated maturase genes necessary to produce nitrous oxide reductase. Core genome analysis placed the genus Halpernia between Kaistella and Planobacterium. Taken together, these findings are sufficient evidence to consider Planobacterium as a separate genus. The SBRL at CDC receives several specimens of this species every year for identification, and it should be recognised as a potential human pathogen. Phylogenetic analysis of the 16S rRNA gene sequences (Fig. S1 ) of all species described as members of the genus Chryseobacterium, along with most other members of the family Weeksellaceae and Flavobacterium aquatile as the outgroup, places the recently published Chryseobacterium reticulitermitis near the base of the tree, near the genus Soonwooa.
number sequenced Other strain numbers Previously described as: Proposed as: Geographic location Source Date isolated tDDH group DDH_# Accession Number CP034159 CP034161 CP034160 CP034158 CP034173 CP034171-CP034172 RJTU00000000 RJTV00000000 RJTW00000000 RJTX00000000 RJTY00000000 RJUG00000000 CP033933 CP033932 Continued 95 224 212 153 148 236 93 142 95 224 nd nd 212 nd nd 71 nd nd 93 142 1973 1984 July, 2013 January, 2004 1987 2008 1998 1982 1979 1983 2004 1980 1982 Beef Testicle Leg wound Peritoneal cavity Blood Blood Blood Urine Green lizard Freshwater lake sediment Lung Blood Calf Sputum Unknown Iowa, USA Indianapolis, Indiana, USA New York, USA Florida, USA Michigan, USA La Louvière, Belgium Darlington, England London, England Lake Daecheong, Korea Indiana, USA New Mexico, USA Midlothian, Scotland Doncaster, England Kaistella carnis Epilithonimonas vandammei Epilithonimonas vandammei Kaistella daneshvariae Planobacterium taklimakanense Planobacterium taklimakanense Epilithonimonas hominis Chryseobacterium sp.
number sequenced Other strain numbers Previously described as: Proposed as: Geographic location Source Date isolated tDDH group DDH_# Accession Number CP033928 CP033925 CP033924 CP033923 CP033922 140 128 63 78 117 C. indoltheticum 63 63 78 78 Unknown Unknown Unknown Late 1960's 1974 Soil Milk bottle rinse Milk bottle rinse, Kidney abscess Urine Unknown Paisley, Scotland Paisley, Scotland Gloucester, England Newcastle-upon- Tyne, England Paisley, Scotland Chryseobacterium indoltheticum Chryseobacterium lactis Chryseobacterium lactis Chryseobacterium nakagawai Chryseobacterium sp. nov. Chryseobacterium carnipullorum Chryseobacterium indoltheticum Chryseobacterium lactis Chryseobacterium lactis Chryseobacterium nakagawai '78 group' '125 group' CL97/78; Hayes S10/1; CCUG 73273 A139/68; CCUG 73271 NCTC 11390, A140/68, F68 F91 CL192/74; CCUG 73268 A103/68; CCUG 73266 G0211 G0197 KC_1864 G0041 G0162 F9942.