dataesr/scientific-paragraphs-categorization huggingface dataset:
Near-real-time validation The near-real-time dataset validation tests are mostly taken from the Argo real-time quality control tests  (Wong et al. , 2009). The goal is to distinguish the spurious measurements from the good measurements and to flag them quickly. The test checks are designed to detect well-known types of errors. A global range test and a regional range test are performed to detect obvious errors with respect to known ocean variability. The bounds of those two tests are very large with respect to the known ocean variability to ensure that no bad flag is incorrectly attributed. A spike test and a gradient test are performed to detect measurement spikes in the temperature and salinity fields. The test is based on the comparison of the temperature and salinity vertical gradient to a threshold.
Spurious detections can, however, occur when profiles located on both sides of a frontal zone are within a correlation radius. Therefore, detected profiles are visually checked by a primary investi-gator (hereafter PI) to distinguish erroneous measurements from correct measurements. Lastly, a quality control based on altimetry comparisons is also performed on a quarterly basis to improve the real-time validated dataset  (Guinehut et al. , 2009). A PI inspection is also performed on profiles flagged as suspicious by comparison with altimetric sea level.
Delayed-time-mode validation tests The delayed-time-mode validation is performed on a yearly basis. This validation framework is based on tests more stringent than the near-real-time validation process, which requires a systematic visual control by an oceanographer. The controlled profiles are those that have not been controlled in the previous version of CORA. Therefore, most of the controlled profiles for a given version of CORA are the profiles measured during the previous year but not controlled for the earlier version. The profiles for which the measurements have been updated or adjusted since the latest version are, however, controlled. Last, some datasets covering the historical period may have been incorporated in the Coriolis dataset, which are then controlled in delayed-time mode in CORA.
The delayed-time mode validation process is schematized in Fig. 4. The profiles to be validated are first checked by the CORA tests. The checks raise an alert flag on suspicious profiles, which are then visually checked. For CORA, the validation checks are applied until all the tests are successful. If a single-check test fails, the profile is put aside for visual check and the following tests are not applied. The profiles undetected by the CORA tests, and thus not visually controlled, are assessed by a second set of tests developed by the CLS company. The suspicious profiles are also visually controlled and manually flagged. Last, all the tested measurements are gathered in the CORA database with the updated flags. A first quality check aims to complement the real-time QC procedure with redundant tests that have a sharper threshold than NRT.
Level disorder and duplicated levels The profiles with a non-monotonous PRES or DEPTH vector are detected, and the PRES or DEPTH vector is flagged in order to be monotonous. This test has been requested by CORA end users, the oceanographic reanalysis community, to have a user-friendly dataset to work with. Most of the detected profiles are indeed measurements with a very slow sinking speed near the surface, giving pressure vector inversion when exposed to the sea surface swell. Most of the detections are thus confined to the surface layer. Exceptions may, however, occur in the case of Black Sea Argo floats for which a recurrent problem of slow sinking speed is found at the subsurface due to the low salinity level of the Black Sea. Last, "hedgehog" type profiles, with very spiky temperature, salinity, and pressure vectors, which are often caused by transmission mistakes on Argo floats, are detected by this test.
Global range The global range test aims to detect obvious measurement mistakes. Temperature measurements under -3 • C or over 43 • C and salinity measurements under 0 PSU or over 46 PSU are detected. This test has a very low detection rate, but it still detects some erroneous profiles each year. Most of them are profiles with a nonclassical shape so that they avoid detection by redundant tests (min-max test or climatological test). A recent example was an Argo float grounded near Mogadishu, Somalia, measuring a temperature exceeding 43 • C, whereas the corresponding pressure was just above 0 dbar so that the measurement avoided the other NRT and delayedtime-mode tests confined to depths between 0 and 2000 m. The following step of the CORA data validation is performed in the Coriolis data center to detect profiles diverging from the known ocean variability.
Each temperature and salinity profile is compared with the minimum and maximum measured value reference profiles. Those profiles originate from reference fields on a gridded mesh with 1 • resolution horizontal hexagonal cells of 20 m thickness. The reference fields are the maximum and minimum measured values on a set of 1. 2 million Argo profiles, vertically interpolated from the surface to 2000 m of depth. The field coverage is increased, especially in the inner seas and in the Austral Ocean, which are badly covered by the Argo network, by CTDs from the World Ocean Database, and sea mammal measurements from the MEOP database. The CORA 5. 2 measurements are compared to the minimum and maximum reference values of the corresponding cell and the upper and lower adjacent cells in the same grid column.
See  Gourrion et al. (2019)  for further discussion on the min-max field. A third validation is performed with the In Situ Analysis System (ISAS) objective analysis tool, following the method developed by  Gaillard et al. (2009). During the objective analysis process, the profile analysis residual is compared to the analysis residual of neighboring profiles. This validation test is similar to the validation performed by the Coriolis data center in near real time with the Argo floats. The scope of the test is, however, extended to other profiles (XBT, CTD, etc. ) and to Argo profiles that have been updated in the Coriolis database too late to be part of the near-real-time validation. A last set of delayed-mode validation tests has been developed by the CLS research and development team and aims to complement the validation tests.
CORA 5. 2 quality control results The relevance of ocean climate studies strongly depends on the accuracy of ocean measurements. Systematic data errors might thus result in biasing the estimation of ocean-state indicators such as the GOHC, the global ocean freshwater content, or the global mean steric height  (Levitus et al. , 2009). Furthermore, random measurement and data error may lead to overestimations of the ocean variability. Therefore, indirectly, one can assess the reliability of the global dataset by estimating the influence of the quality control on global metrics such as the ocean mean temperature and salinity and the associated variability. Two mappings of ocean temperature and salinity based on the CORA dataset measurements are calculated: a raw estimation (GOHCraw) that considers every measurement without taking the data quality flags and a flagged estimation (GOHCflg) that only considers the good and probably good QCs.
The combined mean is then T (i, j ) = p G p (i, j ) T P n P N (i, j ) , (2) with N (i, j ) = P G P (i, j ) n P. (3) The combined variance is estimated with a similar operator: var(T ) (i, j ) = p G p (i, j ) var(T P )n P N (i, j ) , (4) with n P the number of measurements available in the summed grid point, T p-the mean temperature at the grid point, and N (i, j ) the total number of measurements involved in the calculation of a grid point value. The values of r l and r L are set to 5 • longitude and latitude in order to include enough grid points with data in this averaging. To reduce the calculation time of the analysis, each N (i, j ) calculation is performed on a 20 per 20 grid point window. The objective analysis is performed in three steps for the global dataset. A first analysis is performed on a raw dataset, considering all available profile measurements.
All the QC flags are considered good. A second analysis is performed on the same data profiles considering the QC available in NRT mode. A third one is performed on the same profiles considering the QC available in delayed-time mode. The ocean data coverage is sometimes insufficient to perform the monthly objective analysis on the whole ocean. As a result, we have limited this study to the latitude between 60 • N and 60 • S since the ocean data coverage is too sparse out of these limits, leading to random anomalies in the temperature and salinity variability. Figure  5  shows an estimation of the ocean layer covered by the objective analysis as a percentage of the ocean layer surface between 60 • N and 60 • S. It shows that the ocean coverage is higher for temperature than for salinity objective analysis.
The upper layer coverage is very close. It varies from 95 % in 2005 to over 98 % after 2012. The 1475-1525 m depth layer departs from the others since it has a global coverage lower than the others, starting from 65 % in January 2005. It converges to over 98 % after 2014. A monthly variability is observed in the Argo  development period (2005-2010). It is probably caused by the slow arrival of Argo profilers in the southern zones. This behavior lasts until 2012 in the deeper layer. Figure  6  shows the percentage of good and probably good QC flags in the NRT and CORA datasets compared to the RAW dataset. It shows that the yearly tendencies for the proportion of good and probably good flags are almost the same at all depths. In any case, CORA and NRT differ by less than 0. 5 %. The proportion of good and probably good temperature flags varies from a minimum of 92 % in 2006 to a plateau of about 98 % after 2013.
the corresponding number for the NRT and CORA dataset is not. The good and probably good salinity flag rate tendency is opposite to the good temperature flag rate, with a maximum of over 98 % before 2010 and then a decrease to a level of about 94 % with high interannual variability after 2011. The mean 0-50, 75-125, 275-325, 475-525, 975-1025, and 1475-1525 m depth salinity standard deviations analyzed by the method (Eq. 4) from 2005 to 2016 are shown in Figs. 7  and 8. The mean salinity standard deviation is averaged between 60 • N and 60 • S for each dataset analysis. The comparison of the raw dataset analysis with the NRT analysis and the CORA analysis shows the gain in dataset quality resulting from the QC performed. In the raw dataset analysis, numerous random mistakes result in a high average salinity standard deviation.
The raw dataset standard deviation is, however, lower in the early period at almost all levels, despite a rather high global level for the 475-525 m depth layer and a variability spike in late 2006 in the bottom layer. This lower variability level is probably a consequence of the Argo program development from 2005 to 2008, with the low coverage in the southern oceans preventing the emergence of high-level values. During this period, a large seasonal variability is present in the upper layers, varying from 0. 2 PSU during winter to 0. 4 PSU during summer in the surface layer. The peaks in ocean variability are thus correlated with peaks in ocean coverage (see Fig. 5 ). The objective analyses also have a higher proportion of shipborne measurements, CTDs for instance, essentially made during summer, compared to the autonomous measurements in the same years.
We can thus assume that the lower number of profiles during winter does not allow us to correctly sample the subsurface ocean fronts, leading to an underestimated wintertime ocean variability. The increase in the amount of Argo float data from 2005 to 2008 slowly decreased this bias in the ocean variability estimation. The raw dataset surface salinity standard deviation increases during the 2010-2016 period at all depth levels, with a 0. 6 PSU amplitude and spikes up to 1. 2 PSU in 2010 in the surface layer, as well as spikes varying from 0. 9 to 1. 2 PSU in the other layers. The NRT analysis is very close to the CORA analysis before 2008. This behavior is a consequence of the low number of measurements corresponding to this period collected or updated in the database after the validation of the last version of the CORA dataset.
A striking feature is the corresponding spike visible in the NRT analysis and in the raw dataset analysis in late 2010, which suggests that major data errors have not been flagged in the dataset during the NRT validation. Further exploration of this anomaly has shown that a fraction of the larger error bar in the NRT analysis is caused by an issue in the update of delayed-time-mode processed Argo profiles. In a few cases when salinity measurements present large drifts, the Argo PIs can decide that the salinity drift is too high to be adjusted. In these cases, the PI provides to the global DAC a delayed-time version of the profiles with an adjusted temperature field but with a practical salinity field with fill values and a salinity QC field filled with "4" values (bad measurement status). In some cases, the Coriolis data center had updated the profiles by getting the temperature-adjusted field but without creat-ing a salinity-adjusted field.
The available salinity field in the Coriolis data center is therefore the original salinity field that might not have been flagged at 4. In this study, a handful of these profiles, often associated with large salinity measurement drifts (for instance, salinity values on the order of 20 PSU in the Indian Ocean), have produced large error bars in the NRT analysis fields. This issue will be soon tackled in the Coriolis database. The CORA analysis salinity standard deviation slowly varies in time, with 0. 15 PSU in the surface layer, 0. 1 PSU in the 75-125 m depth layer, 0. 08 PSU in the 275-325 m depth layer, and below 0. 05 PSU in the deeper layers. This behavior is a consequence of the delayed-time-mode validation process, which strongly reduces the number of random mistakes in the dataset.
This variability is probably a function of the local data resolution, the oceanic variability, and measurement errors. The slow variability of the CORA salinity standard deviation and its reasonable range suggests that remaining errors in the dataset will not have a large importance. Thus, this product is likely to present a low error amplitude. A closer look at the vertical profiles of the temperature and salinity mean variability (Figs. 9 and 10) shows that the CORA analysis temperature and salinity variability is far smaller than the RAW analysis and the NRT analysis estimation. The depth variability of the temperature and salinity mean variability is moreover closer to the expected oceanic variability, with maximum ocean variability at the surface or close to the subsurface and decreasing variability below the ocean mixed layer depth.
We, however, lack a reference high-quality dataset to compare with to prove that the CORA dataset is not decreasing the global ocean variability by overflagging good data. Indeed, one should keep in mind that most of the flags applied on these profiles are manually applied by physical oceanographers after receiving a detection alert, and the rate of flagged profiles in the CORA analysis is lower than the rate announced for a reference dataset and analysis based on automatic quality control tests  (Gouretski et al. , 2018).
Conclusions The CORA dataset is an extensive dataset of temperature and salinity measurements. Efforts have been made to provide the scientific community with information as close as possible to the physical measurement and to perform a strict quality control on all profiles. The CORA dataset indeed stands out from the EN4 dataset since the delayed-time-mode val-idation is based on automatic detections and systematic PI decisions, reducing the number of mistaken bad flags. In addition to that, the profiles are not subsampled and the time series (TSGs and drifters) are distributed. It also stands out from the WOD dataset since all measurements within a profile are validated in delayed-time mode, reducing the number of mistaken measurements. Moreover, this study develops an innovative method to assess the overall quality of a dataset.
This method shows improvements of the dataset quality flags thanks to Coriolis real-time QC and the CORA delayed-time-mode QC frameworks. This method, however, lacks a comparison with an analysis based on other datasets to ensure that the CORA validation framework is not constraining its description of the ocean variability by over-flagging good measurements. This discussion shall be further pursued. This method is based on the mapping of the ocean variability. It is thus implicit that the ocean sampling is homogeneous and sufficient to perform a monthly analysis. These conditions are met at a global scale and for ocean measurements from the surface to 2000 m of depth since the full deployment of the Argo network. Last, the ocean data coverage is, however, insufficient to have a global coverage before 2005 (see Fig.
3  for the ocean basin data coverage ratio), especially at depth larger than 1000 m between 1990 and 2005 and at depth larger than 500 m before 1990, as seen in Fig. 2. The method will thus have to be adapted to the ocean data coverage to provide a synoptic view of the dataset quality. DOI: https://doi. org/10. 17882/46219TS1 (Szekely et al. , 2019). Author contributions. TS and JG conceived and designed the analysis on the advice of GR. TS, SP, and GR collected the data and contributed to the data validation. TS and JR wrote the article. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "The Copernicus Marine Environment Monitoring Service (CMEMS): scientific advances". It is not associated with a conference.
The flags in the NRT and CORA datasets are indeed the same except when an updated version of a profile is loaded in the database or a new profile is loaded in the Coriolis database. On the other hand, large discrepancies between the NRT and the CORA datasets are recorded between 2009 and early 2012 and between late 2013 and 2016. Another fraction of the discrepancy between the NRT and the CORA error bars is caused by non-Argo profiles updated in the Coriolis database without delayed-time-mode assessment. Most of these measurements are sea mammal profiles in the northern Pacific Ocean or mooring data imported from the GTSPP (Global Temperature and Salinity Profile Program; https://www. nodc. noaa. gov/GTSPP/, last access:.
Ocean Sci. ,15,[1601][1602][1603][1604][1605][1606][1607][1608][1609][1610][1611][1612][1613][1614] 2019www. ocean-sci. net/15/1601/2019/ Data availability. The CORA 5. 2 dataset is dis- tributed by the Copernicus Marine and Environment Monitoring System (CMEMS). Product name: IN- SITU_GLO_TS_REP_OBSERVATIONS_013_001_b.
We performed the exact tests implemented in GENEPOP v4. 6  (Rousset, 2008)  to detect linkage disequilibrium for each pair of loci in each colony and to investigate departure from Hardy-Weinberg equilibrium (HWE) for the whole dataset and for each colony and for each locus under the hypothesis of heterozygote deficit. We used the false discovery rate (FDR) to account for multiple testing  (Benjamini & Hochberg, 1995). The adjusted p-value thresholds after FDR correction (p critical ) were calculated following  Castro and Singer (2006).
We estimated the maximum likelihood pairwise coefficient of relatedness r between pairs of individuals on an absolute scale (0, unrelated to; 1, identical individuals), using the ML-RELATE software  (Kalinowski, Wagner, & Taper, 2006). Indeed maximum likelihood estimate is less biased than commonly used estimators  (Milligan, 2003). The coefficient r corresponds to the probability for each locus that individuals share zero, one, or two alleles that are identical by descent. We used the genetic clusters identified from further clustering analyses (see below) as population references for estimating r within each colony. F I G U R E 1.
Genetic structure was also investigated using several complementary approaches to give a robust cross-validation of our results and prove that the observed genetic signature is robust despite the potential violation of the underlying hypothesis. First, we used the clustering approach implemented in the STRUCTURE program v2. 3. 4  (Pritchard, Stephens, & Donnelly, 2000)  to determine the presence of genetic discontinuities without any a priori knowledge. We determined the most likely number of genetic clusters using the log-likelihood of K and ∆K statistic  (Evanno, Regnaut, & Goudet, 2005)  implemented in the website STRUCTURE HARVESTER  (Earl & vonHoldt, 2012). We used the admixture model with uncorrelated frequencies and an alpha-value of 1/K, as recommended by  Wang (2017)  in the case of unbalanced sampling.
We used the R package pophelper  (Francis, 2017)  to compute the plots. In addition, we also performed a principal component analysis (PCA) implemented in the R packages ade4  (Dray & Dufour, 2007)  and factoextra  (Kassambara & Mundt, 2017). Contrary to STRUCTURE algorithm, this approach does not rely on any specific population genetic assumption including Hardy-Weinberg equilibrium and linkage equilibrium  (Pritchard et al. , 2000). We next used MAPI program (Mapping Averaged Pairwise Information,  Piry et al. , 2016) , implemented in the R package mapi, to detect spatial genetic discontinuity. This approach has low sensitivity to potential confounding effects resulting from isolation by distance (IBD) and does not require predefined population genetic model. It is based on a spatial network in which pairwise genetic distance between georeferenced samples is attributed to ellipses.
| Inference of demographic parameters We first inferred the demographic history of R. ferrumequinum colonies. We used the software MIGRAINE v. 0. 5. 1  (Leblois et al. , 2014)  and the model OnePopVarSize. This model assumes a unique variation starting T generations ago in population size of an isolated panmictic population that was followed by continuous exponential change in population size until the moment of sampling. We then inferred contemporary levels and directions of migration between the main genetic clusters using the program BAYESASS v3. 0. 4  (Wilson & Rannala, 2003). We performed five independent runs of 10,000,000 iterations sampled every 2000 iterations, with a burn-in of 1,000,000. For each run, we calculated the Bayesian deviance using the R script provided by  Meirmans (2014).
| Population structure and genetic differentiation between colonies F ST estimates calculated with and without excluding null alleles (ENA) were very similar, and analyses without juveniles did not substantially change F ST estimates. Therefore, we only reported the uncorrected estimates for the dataset including juveniles. The pairwise F ST estimates between colonies and associated G-tests are presented in Table  S2. Low but significant genetic differentiation was observed between colonies within western France (F ST < 3%, 65. 24% of the G-tests with p <. 05; Table  S2 ). The northern French colony "MON" exhibited higher estimates of pairwise F ST than the other colonies (3. 49% < F ST < 6. 46%, G-tests p < 10 -3 ). Among the French colonies, "MON" was also the colony that exhibited the highest levels of genetic differentiation with the English (F ST > 16%, G-tests p <.