dataesr/scientific-paragraphs-categorization huggingface dataset:
Since the statistical robustness of the extrema estimates depends on the data coverage, the results for this grid no longer systematically decrease with increasing latitude. The resulting detection efficiency is much more homogeneous. Second, the vertical sampling scheme of the various instruments used in the reference databases differs significantly. As a result, the vertical data profiles may not have a value within every pressure bin so that the vertical profiles of minimum and maximum estimates may be highly discontinuous. To avoid such a discontinuity, the measured values are propagated to the missing levels from one level above and below to fill the data gap, except for those gaps due to multiple erroneous observations; a maximum of one missing level is authorized. In this case, and after the iterative QC procedure described above, the profiles are linearly interpolated to the center of the pressure bins located between two consecutive valid measurements, and the iterative estimation procedure is repeated.
This procedure removes most irregularities in the vertical extrema profiles. Third, real uncommon events are often present in the database, but probably not at all locations where they may actually occur. As a result, in some cases, horizontal discontinuities may still appear in these fields, even after iterative and manual QC. It is then reasonable to consider that such an uncommon event might be observed in a near neighbor. Optionally, assuming that the statistics are locally ergodic, a smoothed version of the reference fields is computed a posteriori by replacing each minimum (maximum) value by the smallest (largest) value for the cell itself and its immediate neighbors. In the following, all reported statistics are estimated on this basis, that is, provided on the 100-km hexagonal grid but, for each cell, actually computed from the distribution of data corresponding to that cell together with its immediate neighbors.
1) ARGO A snapshot of the Argo dataset was first downloaded in September 2015 (http://doi. org/10. 17882/42182#42342) from the Global Data Assembly Center (GDAC) (see  Argo 2014). This dataset contained more than 1. 4 million profiles, from nearly 10 000 platforms. Only ascending profiles with delayed-time parameter values are used in this analysis. While Argo provides a relatively homogeneous coverage of the global ocean, it is still sparse near continental shelves and, for lack of deployment opportunities, in the Southern Ocean. Consequently, the dataset is extended using the following datasets.
2) CLIMATOLOGICAL CTD DATASETS The International Council for the Exploration of the Sea (ICES) also provides a high quality CTD database focused on the Northern Atlantic and Arctic Oceans (http://ices. dk/marine-data/data-portals/Pages/ocean. aspx; 13 000 profiles). Ifremer also maintains a database of all CTD acquired onboard its research vessels (http:// donnees-campagnes. flotteoceanographique. fr; 7000 profiles). The Ocean Climate Laboratory (OCL; see https:// www. nodc. noaa. gov/about/oceanclimate. html) updates regularly its World Ocean Database (WOD); we collected their historical dataset of CTD profiles at observed depth levels. These three important climatological CTD datasets are included. Practically, we accessed these data through the Coriolis interface. At the time that the Coriolis website was accessed, 43 000 profiles were available from the WOD.
c. Salinity statistical parameters Once the datasets have been iteratively quality controlled as described above, temperature, salinity and potential density distributions are assembled over all oceanic grid cells and 20-m-thick layers, as described in section 2a. From these distributions, minimum and maximum values, as well as standard statistical moments, are determined. In this section, all of these statistical parameters are presented and intercompared to investigate the consistency of the minimum and maximum estimates. First, some statistical background is recalled to provide some basics and help understand the latter comparison. Then the spatial distributions of data at the surface and 1000 m are presented. Following this, the minimum and maximum fields are introduced and interpreted in terms of validity range; a similarity with third-and fourth-order statistical moments is presented.
Pearson diagram, a tool for distribution classification In (S 2 , K ) space,  Pearson (1905)  defined distribution families. In this diagram, reference analytical laws are identified, either by points (Gauss and Rayleigh), single curves (Student, gamma, inverse gamma, and Weibull) or partially bounded domains (generalized beta and beta prime). Figure  2  displays such a diagram with colored lines corresponding to the location of the Weibull, gamma and inverse-gamma distributions. The distribution of sample skewness and kurtosis is shown with light gray contour lines. In section 2c(6), the Pearson diagram is used to identify an analytical distribution family that represents reasonably well our dataset on the basis of the (S 2 , K ) distribution.
2) NUMBER OF SAMPLES The number of vertical profiles with observations in the surface and in the 1000-1020-m layers are shown in Fig. 3. Due to the Argo network sampling, the spatial coverage is rather uniform over the global ocean. Nevertheless, heterogeneities are present. Higher spatial density appears in the vicinity of the Kuroshio and Gulf Stream regions as they are the closing branch of the subtropical circulation; lower density occurs in areas where the platforms either have difficulty entering due to the large part of their life spent at depth (continental shelves, marginal seas) or are less deployed due to scarce ship routes crossing them (South Atlantic and Southern Oceans). The ICES dataset specifically contributes to increased density in the North Atlantic, north of 508N. The MEOP dataset improves the overall low sampling of the Southern Ocean.
The OCL dataset contribution is particularly obvious through the zonal and meridional high density lines, particularly in the central equatorial Pacific Ocean. It also has an important contribution over the continental shelves. For the 1000-m layer, the spatial   5 )-(  7 ). The pink, blue, and green lines correspond respectively to Weibull, gamma, and inverse gamma distributions. The domain bounded by the blue and black lines corresponds to the generalized beta distribution, and the domain below the green line corresponds to the beta prime distribution. distribution is similar to the one at the surface; differences come from vertical profiles that do not reach such a depth, especially in the intertropical domain (shallower Argo sampling) and in the Southern Ocean (sea mammals do not reach such a depth).
The factor 6 in Eq. (  6 ) allows us to scale NICS similarly to skewness, that is, as a ratio to 1 standard deviation; see Eq. (  4 ). The middle panels in Fig. 5  display surface NICS (left panel) and IWR (right panel) from the quality-controlled dataset. In the surface intertropical Pacific and Atlantic Oceans, the signatures of precipitations and runoff identified in the minimum field (see section 3) are clearly present in the NICS field: relative to the mean value, the validity interval is shifted toward negative values indicating that large fresh anomalies are more likely to occur than salty ones. A striking feature is visible in the Southern Ocean, especially east of the Greenwich Meridian. The subtropical front is a boundary with intense mixing between warm and salty South Indian Central Water (SICW) and fresher and colder sub-Antarctic Surface Water (SASW).
2; such a value is in reasonable agreement with the 99. 7% of data included inside a 6-std interval under the Gaussian assumption (see section 3). With decreasing number of samples, the minimum value has not fully converged to such a value and the percentile rapidly increases. The minimum estimate is less robust and describes a location in the distribution tail progressively closer to the interior. A number of 300 to 400 samples can be considered as a threshold value to ensure a sufficient robustness. This value is in good agreement with the results obtained in section 5 for IWR. Percentiles derived from minimum and maximum estimates have lower spread than those obtained from sample S and K , suggesting again a higher robustness and quality. Indeed, minimum and maximum values are individual samples with robustness primarily depending on the performed QC work focusing specifically on the extreme values of the dataset, see section 2.
Illustration of minimum and maximum usage for QC purposes Raw noisy data from a CTD sensor mounted in the bilge of the One Planet One Ocean (OPOO) sailing boat that participated in the Barcelona World Race (BWR) were kindly provided by J. Salat and J. Salvador, from the Institute of Marine Sciences (ICM), Spanish National Research Council (CSIC). OPOO covered a round-the-world track starting on 31 December 2014 in Barcelona, Spain. The data are now used to illustrate the potential of the minimum and maximum estimates for QC purposes. 5 ) and (  6 ) (red dots). Figure  9  shows the trajectory of the boat and the entire salinity time series, as well as an expanded time period of particular interest here. Under high-hull-speed conditions, the boat rises above the water and air enters the sensor, resulting in negative conductivity and salinity errors.
To further intercompare the two approaches, we now focus on a couple of salinity anomalies occurring with the boat crossing the ITCZ on 11 January and 24 March or sailing through a freshwater pool by the southern tip of South America on 7 March. For the 7 March event, the low-salinity anomaly encountered near Cape Horn similarly impacts the lower bound of the validity interval in both approaches. Nevertheless, for the upper bound, while the maximum value is not sensitive to that fresher water, the classical upper bound is symmetrically shifted up. This is because the method assumes a symmetrical data distribution while the mean value is not affected by the fresh anomaly, which likely has a low occurrence in the reference dataset. The consequence is that the ability of detecting measurement errors associated to positive errors up to 1 psu is severely degraded locally.
In the second case, a similar analysis can be conducted, except that the classical upper bound of the validity interval is impacted less relatively, that is, shifted toward higher values; the fresh anomaly is partly carried by the standard deviation but also by the mean value; in this case, near 58N, the probability of occurrence of such a fresh event is higher as the ITCZ crosses the area twice a year, during its northward and southward migration respectively in May and November. As intuited in section 1, the performance of the classical approach depends on the occurrence of uncommon events in the reference dataset, while the minimum and maximum approach is independent of such occurrence as long as it is larger than zero.
Statistical assessment In the two previous sections, some examples described the added value of the minimum and maximum approach relative to the classical one. Here, we provide a statistical assessment of this added value in terms of number of good detections (GD) and bad detections (BD). A robust validation approach should use independent datasets to derive the reference fields and to validate them using Monte Carlo experiments. We derived a set of reference minimum and maximum fields from a randomly selected fraction of the available dataset (typically 70%-90%), and use the remaining fraction (30%-10%) to assess the procedure, repeating this random split a large number of times to reach the necessary statistical confidence. First, note that, as shown in earlier sections, the robustness of the reference minimum and maximum fields is highly sensitive to the size of the dataset used to build them, and the entire dataset presently available is still insufficient to reach full statistical robustness.
Thus, the split of the available dataset into development and validation subsets degrades the suboptimal version of the minimum and maximum approach due to the degraded reference fields, which is the price of a Monte Carlo validation procedure. This degradation has a much weaker effect on the classical approach as the first-and secondorder statistical moments converge much more rapidly, see Fig. 7. As a consequence, the method accuracy as obtained from the validation results should be considered as a lower bound estimate of the actual accuracy. Second, the number of members in the Monte Carlo experiment is restricted by the high computational cost of each of them, that includes 1) building the reference minimum and maximum fields from the first data subset and 2) running the qualification method for the second subset.
a. Good and bad detections To validate the procedure, the Argo dataset as provided in the global Copernicus Marine Environment Monitoring Service (CMEMS) In Situ dataset (http://marine. copernicus. eu/services-portfolio/access-to-products/? option5com_csw&view5details&product_id5INSITU_ GLO_TS_REP_OBSERVATIONS_013_001_b; credit to EU Copernicus Marine Service Information) is used for validation. The delayed-time QC procedures include both automatic detection and human inspection, leading to a high-quality set of flags. Assuming that the quality of such flags is perfect, they are used, in this section, to evaluate any automatic qualification procedure. The classical approach for different N values and the minimum and maximum procedure are run and the corresponding flags obtained. These flags are categorized as either GD or BD on the basis of their agreement with the CMEMS ones.
The GD statistics are independent of the method, except for the classical approach with N 5 5 or 6 for which the GD number may be reduced by up to 20%, leading to a reduced overall quality of the dataset under such an automatic QC approach. For reference, the percent of all profiles with realtime QC (at Argo program level) is displayed in the same panel. In delayed-time QC, Argo observations may be not only flagged but also corrected to reduce offsets or biases. As such, a significant reduction of the number of errors is expected at this step. Its steady increase since 2012 is fully consistent with the fact that delayed time QC is made available within a delay varying from several months to a few years. For all approaches, the overall agreement with the GD time series, especially in the last years, suggests that, as expected, the overall quality of the dataset degrades slowly from 1% erroneous data to 2% in the last years before the present with decreasing amount of observations controlled by delayed time QC procedures, confirming the overall robustness of our GD statistics.
For the classical approach, it can be seen that the choice between lower or higher N value is a trade-off between 1) maximizing the number of detected erroneous data and 2) minimizing the number of false alarms, which, in fully automatic mode, does discard a significant amount of valid observations, or, when combined with human control, does imply a poor efficiency of the operator work. The validation results for the minimum and maximum approach show a significant impact of the dataset splitting ratio, particularly for bad detections. In the right panels of Fig. 10 , the colored circles indicate that the 90/10 splitting ratio provides the best BD number reduction while the GD number is only marginally reduced. This indicates that the amount of data used in building the minimum and maximum reference fields is more important than the amount used in the assessment procedure.
As such, in the following, we use only the 90/10 splitting-ratio value. The results that should be obtained with minimum and maximum reference fields built from the full dataset can be anticipated from the right panels of Fig. 10  through extrapolation of the results obtained for the 3 values of the dataset splitting ratio. The minimum and maximum approach allows us to somehow uncouple GD and BD statistics: as shown in Fig. 11 , within the 200-500-m and the 500-1000-m layers, the GD numbers are close to those from the classical approach with N 5 4 while the BD ones correspond better to N 5 5; see the right column of Fig. 11. The results are somehow degraded in the 0-200-m and the 1000-2000-m layers. In the 0-200 m, a lower reduction in BD is observed. Given that there are roughly the same number of observations in all layers, the significantly higher variability in the shallow layers implies a reduced statistical robustness of the minimum and maximum estimates.
The ellipse semiaxes are scaled with the corresponding standard deviations. The presented statistics are first computed as the difference from the above-defined reference levels and second are normalized so that the classical-approach results for N 5 6 and N 5 4 have respective coordinates (0, 0) and (1, 1) in the diagram frame. distribution. In the present study, we propose to directly estimating the validity interval bounds as the local minimum and maximum values from the historical dataset, after dedicated manual QC work. This is a refinement of the statistical description beyond secondorder statistics that allows accounting for spatial variations of the historical distribution shape, for example, asymmetry and peak enhancement, see Figs. 5  and 6. Gouretski (2018)  proposed an ad hoc modification of the classical approach based on robust skewness estimates, which only accounts for third-order corrections but does not address the case of fourth-order variations of the distribution shape.
It is demonstrated that, for a similar number of good detections, the new approach allows an important reduction of the number of bad detections. If used as an alert-raising tool combined with human QC, the number of bad detections can be seen as unnecessary use of human time so that its reduction leads to a significant saving of human resources. The success is attributed to the increased accuracy of the minimum and maximum statistical estimates in accounting for previously observed uncommon events when defining a validity interval. On the one hand, such an increased accuracy comes from the fact that a specific uncommon event introduced in the reference dataset will never raise a detection while, in the classical approach, it might be detected depending on its occurrence in the reference dataset and its weight in the estimates of the first-and second-order statistical moments.
On the other hand, this increased sensitivity to rare events requires an extensive and specific manual QC step. It is also evident that, being more selective, the approach may fail more rapidly in areas where the variability is poorly sampled in the reference dataset. Nevertheless, if the method allows significant reduction in the number of erroneous detections to be checked by the operator in delayed-mode QC, this is a good result. The number of erroneous detections is still too high for an implementation in a operational nearreal-time system. This is to be related with the spatial distribution of observations, see Fig. 3 , and the number of grid cells that do not reach a threshold number of samples (300-500). It is clear that statistics will improve at updating the reference dataset with the latest observations; this will help to improve the method performance, that is, reduce the number of detections, progressively with time.
Inside an operational data production system (such as the Coriolis facility), it will be pertinent that all the observations with an alert raised by this automated QC procedure but cancelled by an operator be included regularly in the reference dataset. Future work should aim to further improve the method performance. It will require building some model of the nonsampled variability in order to artificially widen the validity interval. Such extrapolation of the empirical distribution might either be based on an adequate analytical model of the distribution, or on an ad hoc prediction from available moments and quantiles. Acknowledgments. This study has been conducted using EU Copernicus Marine Service Information and was supported by the European Union within the EU Copernicus Marine Service In Situ phase-I and phase-II contracts led by Ifremer.
Extensive literature search In vivo probe substrates for P-gp, BCRP and OAT1/3 were identified from the FDA website and datasheets while for other transporters (OCT, OATPs, MATE1/MATE2-K), no in vivo specific probe substrates were available  (FDA, 2017). For each probe substrate, extensive literature searches (ELS) were conducted in  PubMed and Scopus (1966-June 2019)  according to the EFSA guidance document using search terms related to human PK studies provided in Table 1  (EFSA, 2010). Specifically, data from human PK studies reporting markers of oral (single) or intravenous (bolus) acute (Cmax) and chronic exposure (area under the curve (AUC), clearance) were collected for healthy adults from different geographical ancestry or ethnic backgrounds. In addition, data for inter-phenotypic differences were investigated from the literature for three different SNPs in P-gp (3435C > T, 1236C > T, Table  1  provides a summary of the keywords applied to the ELS.
Standardisation of datasets PK parameters collected from literature were standardised to perform the analyses in a harmonised manner for each parameter while correcting to dose and body weight namely AUC, Cmax and clearance expressed in mg/kg BW, ng. h/ml/dose, ng/ml/dose and ml/min/kg BW. Body weight correction from the parameters were performed using mean body weight (kg) recorded from the study when available or allocating them to the country of origin using data from  Walpole et al. (2012). Kinetic data were often either reported as arithmetic mean (X) and standard deviation (SD) or as geometric means (GM) and geometric standard deviation (GSD). Since PK data are well recognised to follow a lognormal distribution, all PK parameters were transformed, when needed, and expressed as GM and GSD using the following equations: = + GM X CV 1 N 2 (1) = + GSD exp CV ( ln(1 )) N 2 (2) where CV N is the coefficient of variation for normally distributed data: = CV SD X N (3) In some studies, SD was not reported and was derived from the standard error (SE, SEM), CV, or 95% confidence interval of the mean as described previously  (Darney et al.
Results 3. 1. Data collection for P-gp, BCRP, and OAT1/3 2643 papers were retrieved from Scopus and PubMed for seven P-gp probe substrates (dabigatran, digoxin, fexofenadine, loperamide, quinidine, talinolol, and vinblastine) and for the OAT1 and OAT3 probe substrate adefovir and sitagliptin. For BCRP, 1115 peer reviewed publications were retrieved for sulfasalazine and rosuvastatin with 20 papers reporting PK data. 496 papers were considered eligible after the first screening while 354 were then as review articles or publications with scarce information or of poor quality Overall, 142 papers were considered eligible and relevant for data extraction and were included in the database. A full account of the screening procedure, inclusion/ exclusion criteria and data collection is reported in  Darney et al.
(2019). Fig. 3  summarises the flow of information for the available PK studies on P-gp, BCRP and OAT1/3 probe substrates while the full list of relevant peer reviewed publications is provided in supplementary information [A] and the full database can be accessed on EFSA knowledge junction under DOI: 10. 5281/zenodo. 3739015 with a Creative Common Attribute 4. 0 license. Fig. 4  illustrates the raw data for each probe substrate and kinetic parameters reflecting acute oral (Cmax) and chronic exposure Table  1  List of search queries for the Extensive Literature Searches on human kinetic for probe substrates of efflux and influx transporters. TITLE-ABS: term searched only in the title and the abstract of the paper. (clearance and AUC) after intravenous and oral dosing. The amount of data available varied from one substrate to another and route as well as the reported geometric means (GM) for all kinetic parameters due to inter-substrate differences in kinetics.