dataesr/scientific-paragraphs-categorization huggingface dataset:
Data extraction and management We constructed a data extraction sheet for the review and two review authors (RC and JR) used this independently for data collection. These authors were blinded to each other's data; however, they were not blinded to the journal of publication or the trial authors. Two review authors (AB and EM) independently extracted the following information from each included trial: 1. setting of the study 2. sample sizes 3. patients 4. baseline characteristics of patients interventions 5. type of surgery outcomes 6. follow-up points If information was missing from the published paper, we contacted the trial authors. We compared results to check for inconsistencies and resolved disagreements by discussion or, if consensus could not be reached, through adjudication by a third review author (EM).
Assessment of risk of bias Two authors independently assessed the included studies using the Cochrane tool for assessing risk of bias. [19] This tool addresses specific domains: random sequence generation (selection bias),  [20, 21]  allocation concealment (selection bias),  [20, 21]  blinding of patients and personnel (performance bias),  [20, 21]  blinding of outcome assessment (detection bias),  [20, 21]  incomplete outcome data (attrition bias),  [22, 23]  selective reporting (reporting bias). [24, 25] We assessed blinding and completeness of outcome data for each outcome separately. We completed a "Risk of bias" table for each eligible study (see Table  1 , Supplemental Content, http://links. lww. com/MD/E368, which illustrates the "Risk of bias"). We presented our assessment of risk of bias using "Risk of bias" summary figure (Fig.
1 ). For trials using clusterrandomization, we assessed the risk of bias using the following domains: recruitment bias, baseline imbalance, loss of clusters, incorrect analysis, and comparability with individually randomized trials. [19] 9. Measures of treatment effect We expressed the treatment effects as RR with 95% confidence intervals (CI) for dichotomous outcomes. We analyzed continuous data as mean differences or standardized mean differences with standard deviations. When possible, we performed intention-to-treat analyses including all patients according to their original allocation. Where binary data were missing, we performed a worst-case scenario analysis of the main outcome. In this case, we assumed that those participants who were lost to follow-up in the treatment group had the worse outcome, while participants lost to follow-up in the control group had the best outcome.
Assessment of heterogeneity We assessed heterogeneity both by a visual inspection of the forest plot and through examination of x 2 test and I 2 statistic. We considered outcomes with a statistically significant x 2 value at the 0. 10 level and I 2 values >50% to be statistically heterogeneous. In the case of statistical heterogeneity, we then ensured that the data and effect sizes were correct. If they were, we attempted to explore heterogeneity through an analysis of the subgroups. If there was extreme unexplained heterogeneity (e. g. , if I 2 values are over 75% or if there is inconsistent direction of the effects), we did not perform pooling. If there were studies that appeared to be outliers, we conducted an analysis with and without the outliers. If heterogeneity could not be sufficiently explained, we accounted for the heterogeneity by using a random-effects model.
Study characteristics The PRISMA flow diagram for systematic reviews is showed in Figure  2. We identified 510 publications using the literature search strategy. Two reviewers (RC and AR) independently read the abstracts and applied the inclusion and exclusion criteria; 457 records were excluded after reviewing the titles and abstracts, there remained 53 abstracts for full-text evaluation. After full-text assessment, we identified 42 publications that fulfilled the inclusion criteria (see Table  1 , Supplemental Content, http://links. lww. com/MD/E368, which illustrates the "Risk of bias"). We identified no ongoing trials following a search of the metaRegister ClinicalTrials. gov accessed on April 2018. 42 RCTs published between 1980 and 2016 were included in our analysis. Study sizes ranged from 11  [34]  to 1671  [35]  for a total of patients of 11,067.
Implications for research Further studies comparing sutures with respect to staples should focus on conducting high-quality RCTs. Detailed demographic and pathological data should be presented as well as rigorous and standardized tools of evaluation of wound outcomes should be used in particular to evaluate postoperative pain and degree of satisfaction for cosmetic outcome. Such trials should follow a randomized, double-blind design. Finally, it could be useful that in each surgical specialities the research design takes into account the specific predisposing factor for wound complications in order to allow surgeons to choose the optimal skin closure technique on the basis on type of surgery as well as patient's clinical features.
Coral reefs, one of the most diverse marine ecosystem on Earth, have inspired a plethora of trait-based ecological studies, with significant recent efforts to compile trait-based datasets for two major components of this ecosystem: corals and fishes  [22, 23]. For some traits, such as maximum body size in fishes, the compilation process is simple because unidimensional, quantitative data (e. g. maximum total length) are compiled in publicly accessible databases; however, when it comes to species' diet or behavior, obtaining consensual data is much more difficult. For example, dietary data are multidimensional (i. e. various prey items can be recorded across individuals), ontogenetically variable (i. e. diet differs between juveniles and adults), spatially variable (i. e. species may show dietary plasticity across locations), and prone to methodological differences and observer bias.
The classification of species into functional groups has advantages for our understanding of ecological patterns  [30, 31]. However, the lack of agreement and the limited transparency of traitbased datasets can conjure skepticism and inhibit the emergence of general patterns. Here, we quantify expert disagreement in the definition of reef fish trophic guilds and propose a novel, transparent, and quantitative framework to delineate trophic guilds. Using coral reef fishes as a case study, we compiled all quantitative, community-wide dietary analyses from several locations across the Pacific and Caribbean and used network analysis to define eight modules that correspond to trophic guilds. We then examined phylogenetic niche conservatism with a phylogenetic Bayesian multinomial model that predicted trophic guilds to the global pool of coral reef fishes, including measures of uncertainty.
Assessment of expert agreement We systematically searched Google Scholar, including papers since 2000, using the following keywords: "coral reefs" AND "reef fish" AND ("fish community" OR "fish assemblage") AND "diet" AND ("functional group" OR "functional trait" OR "functional entity" OR "trophic guild" OR "trophic group"). The results were individually assessed to find data on trophic guilds. We only considered studies performed at the community level that targeted all trophic levels. Most studies were excluded because they only included specific families or groups, or the data were not provided with the publication. We often found redundant results, with groups publishing several papers using the same classification scheme. In those cases, only the first reference was retained. We contacted authors when trophic classifications were widely used across the literature, but data were not provided with the publications.
Data collection on fish gut contents To provide a quantitative definition of trophic guilds for reef fishes, we collected gut content data across the literature at the individual or species level for Chondrichthyes (i. e. cartilaginous fishes) and Osteichthyes (i. e. bony fishes). We obtained dietary information from six published works:  Hiatt & Strasburg (1960)  for the Marshall Islands  [38] ,  Randall (1967)  for Puerto Rico and the Virgin Islands  [39] ,  Hobson (1974)  for Hawaii  [40] , Harmelin-Vivien (1979) for Madagascar  [41] , and  Sano et al. (1984)  for Okinawa  [42]. In addition, we provide hitherto unpublished data on the gut contents of 3,015 individuals collected in New Caledonia from 1984 to 2000. All dietary information was based on visual gut content analysis where prey preference was quantified as volumetric percentage or item frequency.
The data were standardized and analyzed as proportions. To our knowledge, the compiled dataset represents the first compilation of detailed coral reef food webs across ocean basins. A total of 13,961 non-empty fish guts belonging to 615 species were analyzed, and more than 1,200 different prey items were described in the original datasets. First, fish species and family names were taxonomically verified and corrected with the R package rfishbase  [43]. Only species with at least ten non-empty guts were kept for further analysis. The taxonomic classification of each prey item was then obtained, and all poorly informative (e. g. unidentified fragments, unknown species) and redundant items (e. g. "crustacea fragments" when co-occurring with an item already identified to lower taxonomic level such as "shrimp") were discarded.
Prey identification was highly heterogeneous across the six datasets, differing in taxonomic level and the use of common or scientific names (e. g. crabs versus Brachyura). In order to make the six datasets comparable, prey items were grouped into 38 ecologically informative prey groups (Table  S1 ). Items were generally assigned to groups corresponding to their phylum or class. Due to the high diversity and detailed descriptions of crustaceans, they were assigned to the level of order or superorder. Most groups follow official taxonomic classifications except for "detritus," "inorganic," and "zooplankton. " In the West Indies dataset  [39] , items labelled as "Algae & Detritus" were assigned to both of the categories "detritus" and "benthic autotroph," and the percentage was equally divided in two.
Definition of trophic guilds After data cleaning, we compiled dietary information for 615 species. Of those species, 516 were present in only one location, 66 were collected in two locations, 25 in three locations, 7 in four locations, and only 1 across five locations. Before running an analysis at the species level, we tested whether there was a strong regional signal for species present across more than one location. We created a quantitative bipartite network where fish species at each location were linked to the 38 prey groups. This network was weighted so that edge weights represent the proportional contribution of each prey group to the diet of a species at a given location. In order to identify network modules that correspond to reef fish trophic guilds and their preferred prey, we used the maximization of the weighted network modularity based on weighted bipartite networks  [44].
We used uninformative priors and ran the model for three chains, each with 6,000 iterations and a warm-up of 1,000 iterations. We visualized the fitted probabilities for each trophic guild with a phylogenetic tree, including the 535 species with verified phylogenetic positions using the R package ggtree  [49]. Next, we used our model to predict the most likely trophic guild for the global pool of reef fish species. For the extrapolation, we selected all species within reef fish families with more than one representative species (but we also included Zanclus cornutus, which is the only species in the family Zanclidae), which resulted in 50 families. Further, we only selected species with a maximum length greater than 3 cm, which was the maximum size of the smallest fish in our compiled database.
This selection process resulted in a list of 4,554 reef fish species. Currently, no streamlined method exists to predict traits for new species from a phylogenetic regression model. We circumvented this issue by extracting draws of the phylogenetic effect ( =6IJ×-) for each species included in the model. We subsequently predicted the phylogenetic effects for missing species with the help of the function phyEstimate from the R package picante  [51]. This function uses phylogenetic ancestral state estimation to infer trait values for new species on a phylogenetic tree by re-rooting the tree to the parent edge to predict the node. We repeated this inference across 2,000 draws. Per draw, we randomly sampled one of the one hundred trees. Then, we predicted the probability of each species to be assigned to each diet category by combining the predicted phylogenetic effects with the global intercept and slopes for maximum body size for each draw.
Phylogenetic signal To evaluate the significance of the phylogenetic statistic value (δ = 9. 37), we applied a bootstrapping approach and quantified δ after randomly shuffling the trait values 100 times. The median δ of these null models was 0. 000199 (95% confidence interval [0. 000196, 0. 000204]), indicating a strong phylogenetic signal associated with the eight trophic guilds. Phylogeny and maximum body size were sufficient to correctly predict the trophic guild of 97% of the species in our dataset. For most families, there was strong phylogenetic conservatism, which resulted in the high confidence of these predictions (Fig. 4 ). Within some families, however, closely related species displayed distinct dietary preferences. The uncertainty around these family-level predictions was higher, as showcased by high negentropy values for families such as Balistidae, Diodontidae, and Labridae.
Trophic guilds are among the most commonly applied traits to assess ecosystem functioning because they directly relate to energy and nutrient fluxes across trophic levels. Thus, our standardized framework to quantitatively assign trophic guilds across coral reef fishes represents a major step forward for coral reef functional ecology, while heeding the call for openly-accessible, reproducible trait databases  [22, 55, 75]. As trait-based ecology continues to be used to examine disturbances and implement management strategies, our cohesive and accessible framework to predict reef fish trophic guilds can provide key insights into the trajectory of coral reef communities. Coupling our trophic guild assignment framework with predictive models could spur the emergence of an early detection system to forecast shifts in ecosystem functioning  [30].
Further, our results can serve as the foundation for an online platform that permits researchers to collate, update, and utilize trait-based data on coral reef fishes. Similar to current initiatives across the entire tree of life  [55] , we envision the creation of an online, usermaintained dietary database to facilitate collaboration and traceability in trait-based reef fish research. One challenge will lie in merging visual fish gut content analysis databases with molecular data, such as gut content DNA metabarcoding (e. g. [60] ), and biochemical data, such as stable isotope analysis (e. g. [76] ), and short-chain fatty acid profiles (e. g. [77] ), which indicate nutritional assimilation rather than the simple ingestion of prey items  [58]. Despite this challenge, accessibility to a large breadth of reef fish dietary information would improve our framework.
Acknowledgements This work was supported by the  Centre de Recherche Insulaires et Observatoire de l'Environnement (CRIOBE) in Perpignan, France. We thank all the researchers who have made their fish gut content data and/or trait-based fish trophic guild assignments publicly available for use by the wider scientific community. All the relevant data used to perform the analyses and the results of our extrapolation will be made available once the manuscript is published in a scientific journal.
Funding This research was funded by the  BNP Paribas Foundation  ( Reef Services  Project) and the  French National Agency for Scientific Research (ANR ;  REEFLUX Project ;  ANR-17-CE32-0006 ). This research is product of the  SCORE-REEF group  funded by the  Centre de Synthèse et d'Analyse sur la Biodiversité (CESAB) of the Foundation pour la Recherche sur la Biodiversité (FRB)  and the  Agence Nationale de la Biodiversité (AFB). VP was supported by the  Institut Universitaire de France (IUF) , JMC was supported by a  Make Our Planet Great Again Postdoctoral Grant  (mopga-pdf-0000000144), and JC was supported by the  French Polynesian Government  ( RisqueRecif  project).
Realistic ocean state prediction and its validation rely on the availability of high quality in situ observations. To detect data errors, adequate quality check procedures must be designed. This paper presents procedures that take advantage of the ever-growing observation databases that provide climatological knowledge of the ocean variability in the neighborhood of an observation location. Local validity intervals are used to estimate binarily whether the observed values are considered as good or erroneous. Whereas a classical approach estimates validity bounds from first-and second-order moments of the climatological parameter distribution, that is, mean and variance, this work proposes to infer them directly from minimum and maximum observed values. Such an approach avoids any assumption of the parameter distribution such as unimodality, symmetry around the mean, peakedness, or homogeneous distribution tail height relative to distribution peak.
To reach adequate statistical robustness, an extensive manual quality control of the reference dataset is critical. Once the data have been quality checked, the local minima and maxima reference fields are derived and the method is compared with the classical mean/variance-based approach. Performance is assessed in terms of statistics of good and bad detections. It is shown that the present size of the reference datasets allows the parameter estimates to reach a satisfactory robustness level to always make the method more efficient than the classical one. As expected, insufficient robustness persists in areas with an especially low number of samples and high variability.
(2018). Model simulations can provide large and homogeneous sampling, but these models are not a complete version of reality, due to their imperfect representation of the full dynamics and numerical imprecision. How can these predictions be improved using observations that sample an unfiltered reality but with heterogeneous and incomplete sampling? To answer this question, two main activities have received the most attention: 1) development and maintenance of in situ observation networks and 2) design of adequate numerical strategies for data assimilation. In Europe, within the Copernicus Marine Environment Monitoring Service, these activities are conducted at global and regional scales, both in real time and delayed time. At the global scale, Mercator-Océan carries out the modeling and assimilation activities while Coriolis is involved in the observational ones.
Analysis of the results obtained with the proposed approach indicates some interesting ability in the early detection of systematic errors. For most oceanographic observations, random errors are usually at least one order of magnitude smaller than gross errors. Basic QC procedures usually check for errors in platform identification, date, location, value, digital encoding or stuck values, based on global criteria. Other test categories focus on the temporal and spatial consistency of data subsets. Typical tests on temperature/salinity data detect frozen values or sudden changes in time series, as well as spikes, unrealistic gradients or density inversions in vertical profiles. Possible horizontal inconsistencies are often addressed through comparison with local statistics from a climatological reference dataset, checking that a given value lies within a validity range: X min # X # X max , (1) where X stands for the relevant variable and [X min , X max ] defines the range of valid values.
Despite its wide use in QC procedures for hydrographical datasets, the approach described by Eq. (  2 ) implicitly assumes that the data are symmetrically distributed around a single modal value. While tuning the N value may account for nonzero kurtosis, it cannot properly represent skewness. Figure  1  schematically illustrates this point. It is clear that this approach has a single degree of freedom, the N parameter, that is insufficient to achieve the two objectives: improving the number of false alarms degrades the number of good detections, and vice versa. As pointed out by  Ingleby and Huddleston (2007)  in their discussion, ''in any QC system, there is a balance between trying to reject all 'bad' observations and retain all 'good' ones, different users might require a different balance.
Uncommon events may be labeled as erroneous if they are too far from the mean, that is, at a distance larger than N 3 std, even if they are realistic and included in the climatological dataset used to build the reference mean and standard deviation values. In this paper, a different statistical estimator is used to define the boundaries of the validity interval in Eq. (  1 ). The main characteristics of the method are as follows: d Beyond global-range or basin-range QC procedures, the objective is to detect those gross errors that lie outside a local validity interval. d The validity interval bounds are inferred from the minimum and maximum values found in a reference climatological dataset. d Random measurement errors are assumed to be much smaller than the observed variability (high signal-tonoise ratio).
Building the minimum and maximum reference fields a. Strategy for local extrema estimation The extreme values of a climatological dataset can be used to define an efficient validity interval for a given parameter. Indeed, it guarantees that an uncommon, but realistic, event, even if observed only once, will not be discarded when observed again in the future. Nevertheless, estimation of extreme values turns out to be a nontrivial challenge for in situ observations subject to errors with various origins and magnitudes. As minimum and maximum values are extremely sensitive to measurement errors, an adequate strategy must be set up: it should be manual, iterative and based on the spatial consistency of the resulting minimum and maximum fields. The following steps are adopted: 1) All prior quality flags are discarded.
Depending on the dataset, some nonlocal quality checks may be applied (see section 2b). 2) Preliminary minimum and maximum values are computed for bins of longitude, latitude and pressure. As expected, these values are very noisy. For all minimum and maximum values, the associated measurement information is stored. 3) Within each geographic bin and pressure level, T/S/s fields are displayed and visually scrutinized. For all minimum (maximum) values judged as significantly smaller (larger) than their immediate neighbors, the corresponding T/S/s vertical profiles are displayed together with 1) all profiles from the same geographical box and 2) all profiles from the same platform. A decision is then made to accept these data as a realistic uncommon event or to reject (flag) them. 4) Minimum and maximum values at all pressures are recomputed.