dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the spatial autocorrelation that underlies numerous methods for the spatial interpolation  (Cressie, 1993) , data predominantly collected from contiguous geographical areas (in the case of both dry and rainy seasons) exhibit a higher degree of correlation and thus enable good predictability from the training to the testing datasets. This explains the inverted "U" curve by displaying the relationship between the RMSE and the clear-sky ratio for each month in the study period (Figure  7a ). The model's performance is the highest in the rainy winter, as the model is mostly evaluated in the south. Its performance falls in the transition period and then rises again in the summer, as in the summer the model deals with the entire country.
| CONCLUSION In this study, we proposed a two-stage machine learning based approach to estimating near-surface air temperature (T a ) from remotely sensed surface skin temperature (T s ). It enables a seamless estimation of T s and T a at high resolution in both time and space, while achieving good performance and computational efficiency. The novel method takes advantage of an extensive catalogue of geospatial data sets that are becoming increasingly available at a global scale, endowing it with excellent scalability and transferability. Though focusing specifically on Israel, the model presented in this study can be easily replicated to any other regions where comparable geospatial databases are available. The outcomes help further minimize exposure misclassification in epidemiological studies and may also benefit a wide range of stakeholders including policy-makers, urban-planers, and insurance firms.
2. 5. 1 | Stage 1 model: Imputation of MODIS T sGiven the large volume of the MODIS T s data available for training (on average > 33 million instances each year), the random forest regression was applied on data of each month individually to the missing values in the MODIS T s. Predictor variables for the Stage 1 model are T s from the SEVIRI data set, hour of overpass, and spatial coordinates (latitude and longitude), as well as synoptic variables from ECMWF's ERA5 reanalysis datasets including skin temperature (skt), 2 m air temperature (2 t), level 1 soil temperature (stl1), boundary layer height (blh), total cloud cover (tcc), and wind speed. The acronyms in parentheses refer to the short names of parameters in the ECMWF nomenclature. The Stage 1 model is expressed below as Equation2. long:,lat:,skt ERA5 ,2t ERA5 , blh ERA5 , stl1 ERA5 , tcc ERA5 , wind ERA5 !.
ACKNOWLEDGEMENTS This study was funded by the  Israel Ministry of Science, Technology and Space , under contract #  63365 , and the  Effects of Urban Microclimate Variability and Global Climate Change on Heat-Related Cardiovascular Outcomes  in the  Semi-Arid Environment of Southern Israel grant  ( MOST-PRC 2018-2020 ). Bin Zhou  is supported by the postdoctoral scholarship of the  Kreitman School for Advanced Graduate Studies of the Ben-Gurion University of the Negev  and the  PBC   Fellowship Program  for outstanding Chinese and Indian post-doctoral students. Ian Hough  is supported by a grant from  Grenoble Alpes University and Ben Gurion University of the Negev. Allan C. Just  is supported by  NIH  grants  P30ES023515  and  R00ES023450. We thank  EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF)  for providing SEVIRI LST,  NASA LP DAAC for MODIS LST ,  Dr.
In our study, we implemented an array of pumps that generated water circulation in the basin, and contour currents along the slope (Fig. DR1  in the GSA Data Repository 1 ). In our first experiment, the turbidity current flowed through standing water without contour currents. Then, three more experiments were carried out maintaining a similar preparation of the turbidity current, but with three different contour-current velocities (10, 14, and 19 cm s -1 ), using, respectively, 1, 2, and 3 pumps (Fig. DR2 ). The velocity of the contour currents and turbidity currents was measured using a Signal Processing SA (https:// www. signal-processing. com) UDOP 4000 velocimeter, which was located in the middle of the slope in the channel thalweg. This velocimeter measured time series of velocity profiles of the across-and along-slope velocity component (Figs.
Miramontes' postdoctoral fellowship was co-funded by TOTAL (Paris, France) and IFREMER (Institut Français de Recherche pour l'Exploitation de la Mer) as part of the PAMELA (Passive Margin Exploration Laboratories) scientific project. The PAMELA project is a scientific project led by IFREMER and TOTAL in collaboration with Université de Bretagne Occidentale, Université Rennes 1, Université Pierre and Marie Curie, CNRS, and IFPEN (IFP Énergies Nouvelles). This research was funded by the Joint Industry Project (JIP) associated with "The Drifters Research Group" of the Royal Holloway University of London (UK), and it is related to the projects CTM 2012-39599-C03, CGL2016-80445-R, and CTM2016-75129-C3-1-R. Han de Witte provided essential technical assistance to the experiments. Mike Tilston is acknowledged for sharing his experience with the UDOP 4000 velocimetry equipment.
, Crumlin, UK), and haptoglobin was determined by a colorimetric method (Tridelta, Maynooth, County Kildare, Ireland). All determinations were performed with an Olympus AU400 analyzer (Beckman Coulter, Hamburg, Germany). Cortisol, insulin, and kynurenine were determined by ELISA (salivary cortisol ELISA and insulin ELISA, DRG Diagnostics, Marburg, Germany; l-kynurenine ELISA, Immusmol, Bordeaux, France) according to the manufacturer's instructions. The iEMS Reader MF reader (Thermo Scientific, Waltham, MA) with the Ascent software (version 2. 6, Thermo Scientific) was used. From 2 d before starting Trp supplementation to 25 d of study, pendant data loggers (Hobo Pendant G Acceleration Data Loggers, Onset Computer Corp. , Pocasset, MA) were placed on the right hind leg of each calf using cohesive bandages (Eurimexflex, Divasa Farmavic, Barcelona, Spain) to record position measurements every minute to evaluate calf lying behavior.
Data collected for the Hobo data loggers were recovered and processed as described in  Yunta et al. (2012)  to calculate daily lying time, number of lying bouts per day, and side of recumbency. Feeding and lying behaviors and oral stereotypies were monitored at 2, 4, 9, 11, 16, 18, 23, and 25 d of study for all calves 1 h after the morning feeding using 5-min scan sample technique recording the following behaviors: lying, standing, eating solid feed, drinking water, ruminating, nonnutritive oral behaviors (when calves were licking any surface or tongue rolling), suckling the neighbor calves, and vocalizations.
Statistical Analysis Performance, lying behavior, and blood data were analyzed using a mixed-effects model accounting for the fixed effects of treatment, time of measurement, and their 2-way interaction, plus the random effect of calf. Time entered the model as a repeated measure using an autoregressive variance-covariance matrix. Initial BW was used as covariate. Blood data were analyzed separately at 0 and 3 h, accounting in the effect of week of sampling using the same model described above. Also, to assess the potential effect of time relative to feeding on blood metabolites, blood data from all weeks were averaged by time relative to feeding, and the same mixed-effects model described above was run using time (hour relative to feeding) instead of week as a repeated measure. Scan sampling observations were summarized as the number of times each calf performed a given behavior.
ACKNOWLEDGMENTS This research was supported by the Spanish Ministry of Economy and Competitivity (AGL2015-68463-C2-1-P and AGL2015-68463-C2-2-P). A partial support was also received from the Centre de Recerca de Catalunya (CERCA) program from Generalitat de Catalunya (Barcelona, Spain). We also thank Jordi Casino, Clara Jiménez, and Àlex Campreciós from the Institut de Recerca i Tecnologia Agroalimentàries for their help with calf management and care, Granja Murucuc for providing the calves, and Laura Arroyo from Universitat Autònoma of Barcelona (UAB) for collaboration with laboratory analyses. Natalia Yeste is recipient of a fellowship from the Spanish Ministerio de Ciencia, Innovación y Universidades. The authors have no conflicts of interest. 2 T 0 = effect of 4. 5 g/d of Trp supplementation before the milk replacer morning feeding; T 3 = effect of 4.
For all patients, pre-operative PTA (Pure Tone Audiometry) and post-operative PTA (1 year or more after surgery) were collected. All audiometric examinations were performed by certified university clinical audiologists from the same department. Speech discrimination data was not available for all patients, and thus were excluded for the analysis. Data were collected and analyzed according to the recommendations of the committee for Hearing and Equilibrium of the American Academy of Otolaryngology -Head and Neck Surgery  [13]. The means of the thresholds for bone conduction at 0. 5, 1, 2, and 3 kHz were used to form a four-tone pure-tone average. The functional results were evaluated by comparing the pre-operative and 1-year post-operative bone conduction threshold level averages.
Therefore, estimates of mortality in this specific population are necessary to correctly estimate HIV indicators in youth aged 15-24 years. The objective of this study was to first provide incidence rates for mortality and second, incidence rate ratios for mortality in YPHIV compared with YNPHIV, by region and by sex during the periods of 10-14, 15-19, and 20-24 years of age, using programmatic data from the multiregional pediatric International epidemiology Databases to Evaluate AIDS (IeDEA) collaboration.
Outcomes and key definitions We compared incidence rates of mortality in two distinct populations: YPHIV, defined as having enrolled in care before the age of 10 years, and YPNHIV, defined as having enrolled in care at at least 10 years of age. In the absence of data on mode of HIV transmission in most of databases, we selected care entry before age 10 years as a conservative proxy on identifying those infected through mother-tochild transmission. Given that some YPHIV may enter care after age 10, we thus conducted a sensitivity analysis using age less than 15 years at enrolment as a proxy age for identifying perinatal infection. Loss to follow-up (LTFU) was defined as having no clinical contact for at least 365 days; in the case of LTFU, follow-up was censored at the date of last clinical contact.
In multivariate analyses IRRs indicating lower mortality rates in YPHIV compared YNPHIV remained significant in East and Southern Africa, the two largest regions of our database. However, in other regions, differences in mortality rates between YPHIV and YNPHIV were no longer significant, if not contrary to those observed in the primary analyses in 20-24-year-old women, with higher mortality rates in YPHIV compared with YNPHIV. In addition to the above risk of misclassification bias, this study presents other limitations. First, there may be a selection bias in the YNPHIV group. Indeed, a considerable number of adolescents and youth living with nonperinatally acquired HIV are unaware of their status and remain undiagnosed; those who enroll in care are mostly either pregnant adolescent female individuals, potentially healthier, or symptomatic men, and therefore possibly sicker than the overall population of YNPHIV.
Acknowledgements We acknowledge all the children and their families followed up in the participating pediatric centers. We also thank the staff from all participating pediatric centers. We warmly thank all the investigators and pediatric coordinators from the IeDEA regions contributing to the project:  Asia-Pacific (Annette Sohn ),  CCASAnet-Latin America  ( Catherine McGowan ,  Jorge Pinto ,  Regina Succi ,  Daisy Machado , and  Bill Pape ),  Central Africa  ( Marcel Yotebieng  and  Andrew Edmonds ),  East Africa  ( Kara Wools-Kaloustian ),  Southern Africa  ( Mary-Ann Davies ),  West Africa  ( Francois Dabis and Val eriane Leroy ), the  IeDEA Pediatric Working Group  ( Rachel Vreeman , Chair). A full list of acknowledgments is available in Additional file 2 (Supplemental Digital Content 2; http://links.
Materials and Methods For this study, we used satellite and in situ data for the analysis of sea level anomaly (SLA) trends in the South Atlantic. On a regional scale, SLA can be represented as  [2] : SLA = SH + SM + atmospheric loading + static terms ( 1 ) where SH is the Steric Height, the sea level variability due to changes in the column density (the sum of thermal expansion and salinity contraction) and SM represents the sea level variability due to ocean mass. The static terms refer to ocean loadings that do not generate dynamic responses to sea level such as the inverted barometer (IB) effect  [30]  and GIA  [31]. Here we focus on SLA, SH and SM trends in the South Atlantic, and we discuss the contribution of GIA. For the matching period, we calculated the linear trend with and without the seasonal cycle.
The significance of the trends was calculated with a Student's t-test with a level of confidence of 95%  [32]. Note that the spatial pattern of sea level trends maps were done simply by removing the seasonal cycle and computing the linear regression at each grid point. For the datasets described, we estimated the uncertainties as  [33] : 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 = �� 𝜎𝜎 𝑖𝑖 2 + 𝜎𝜎 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 2 ( 2 ) where i is the uncertainty associated with the time series and spread is the standard deviation of the ensemble mean (e. g. , the standard deviation of the two SLA datasets).
Sea Level Anomaly For the SLA trends, we used two data sets: (1) CMEMS: gridded sea level anomaly maps of ¼ of a degree and daily resolution produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS, https://www. marine. copernicus. eu). The delayed time all-sat (DT all-sat) product is used because it is more precise than near real-time data and has the best possible spatial and temporal sampling. In fact, this product was successfully used to study sea level variability in the South West Atlantic (e. g. ,  [18, 19, 34, 35] ). The SLA maps are constructed merging all the available satellite missions (TOPEX/POSEIDON, Jason-1, Geosat Follow-on, Jason-2, Envisat, ERS-1, ERS-2, Cryosat-2, Saral/Altika, Jason-3, Sentinel-3A, HY-2A). There are more than three satellites 70 percent of the time, which increase spatial resolution and improve coverage in high latitudes.
Ocean Mass Thanks to the Gravity Recovery and Climate Experiment (GRACE) mission, it is possible to analyze the sea level variability due to mass change. The mission was launched on 17th March 2002 and ended on 12th October 2017. GRACE consisted of two satellites monitoring the terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure variations. Three different data sets are available from http://grace. jpl. nasa. gov: the Center for Space Research of the University of Texas (CSR RL06), the Deutsches Geo Forschungs Zentrum (GFZ RL06) and the Jet Propulsion Laboratory (JPL RL06). They are corrected by GIA based on the model from  [47]. In addition, standard corrections for geocenter (degree-1), C20 (degree-20, earth oblateness) and C30 (degree-30) are incorporated.
Sea Level Trends and Fronts The spatial patterns of the non-seasonal SLA trend for the period 1993-2017 from CMEMS and CSIRO are displayed in Figure  2. CMEMS SLA was corrected by GIA in order to produce a SLA closer to the SLA of CSIRO. Both non-seasonal SLA trend maps, CMEMS and CSIRO, present a similar spatial pattern (Figure  2 ): the trend values are positive in the entire region, with relatively high values in the latitudinal band between 33° S and ~50° S, in good agreement with the results obtained by  [17]. Except in a few regions in the CMEMS data set, the non-seasonal SLA trends are significant with a 95% confidence level. This result did not change significantly when a level of significance of 90% was considered. The SAMSL trends are (2. 91 ± 0. 46) mm/yr and (2. 73 ± 0. 12) mm/yr for CMEMS and CSIRO, respectively.
These non-seasonal SLA trends are not significantly different from the global value obtained from the ensemble of six altimetry datasets (3. 1 ± 0. 3 mm/yr  [50] ). CMEMS shows finer spatial structures compared to CSIRO (Figure  2 ). The differences observed are due to different corrections applied and to the different number of satellites considered to produce each product  [51]. Indeed, CSIRO has a one-degree spatial resolution, and uses only two satellite missions, while CMEMS has a ¼ of a degree spatial resolution and considers all the available satellite missions. This impacts on the spatial wavelength that the datasets can resolve. [52]  analyzed the wavelength spectra of different altimetry products, including CSIRO and CMEMS. Their result showed that the CSIRO SLA trend has less power than the CMEMS SLA trend at a wavelength of less than 10 3 km and within the study region mesoscale features with lower spatial scales dominant  [25, 53].
To analyze the regional sea level trends, we removed the SAMSL value from the SLA (2. 91 mm/yr for CMEMS and 2. 73 mm/yr for CSIRO). The results are displayed in Figure  3. As in Figure  2 , the large spatial patterns of non-seasonal SLA trends derived from CMEMS and CSIRO are similar. Both datasets display a relative maximum between 50° W and 0° E and 33° S and 50° S with trends of the order of 2-3 mm/yr. In the case of CMEMS, there is also a relative maximum trend in the South West Atlantic, in a region marked by a dark green line, with trends higher than 4 mm/yr (Figure  3 ) that CSIRO does not capture. This region is characterized by variabilities lower than 10 3 km associated with a mesoscale  [25]  that CSIRO cannot resolve  [52]. The large positive trends in the region, marked by a dark green line, correspond quite well with the extended BC region, where a positive and significant SST trend was found  [23].
In fact, superposed to the annual variability, the SAF (ADT contour of 5 cm) and PF (ADT contour of -40 cm) are showing a significant poleward movement (Figure  4a ) as well (Table  1 ). This is consistent with the poleward migration observed in the ACC by  [56]. We suggest that the southward shift of the fronts involves the advection of waters that might explain part of the sea level trends in the Zapiola gyre area (between the SAF and STF) and the region between 25° W and 0° E. Our hypothesis is that the advected waters will change the density of the water column and the contribution to the non-seasonal SLA trend will be related to the steric height. A detailed study of the role of advection in the non-seasonal SLA trends could be addressed with a three-dimensional velocity dataset. Unfortunately, there are not enough in situ observations to perform such a study in this region.
Both time series display a similar sea level variability. Yet, more discrepancies are observed than in the seasonal variability (Figure  5c ). Furthermore, the SAMSL trend is lower than the sum of ocean mass and steric height trends (Figure  7 , Table  2 ) and their intervals slightly overlap. Part of this result might be due to the uncertainties of the datasets. The uncertainty in the sea level trend has been studied through the analysis of the error budget of satellite data (e. g. ,  [38, 42, 44, 60] ). Considering a global or a regional approach might also change the results, on a regional scale,  [44]  found that the uncertainty of the trend is slightly lower than 3 mm/yr; on a global scale the uncertainty is almost one order of magnitude lower (0. 3-0. 4 mm/yr)  [2, 60]. In addition to the uncertainties in the datasets, the interannual variability associated with other drivers, such as wind, might contribute to the sea level budget, as well as the steric height at a layer deeper than 2000 m.
The latter is not significant on global scales since the sea level budget is almost closed with a small residual  [2]. More recently,  [52]  studied the sea level budget on at the basin scale using the most up-to-date observation products. The budget for an area larger than that considered in this study closes within two standard errors (~0. 62 mm/yr). The difference with our result is that they included the abyssal steric height (depth > 2000 m), obtaining a trend of 1 mm/yr. The use of different datasets is also a factor to take into account for the ocean mass trend calculation. We obtained a trend of 2. 22 ± 0. 21 mm/yr from the mean of CSR RL06, JPL RL06 and GFZ RL06, while  [52]  estimated a trend of 1. 78 ± 0. 22 mm/yr derived from RL06 JPL mascon. Indeed, GIA correction is a source of uncertainties  [52]  and the GIA model changes between GRACE products.
This negative trend in steric height is not fully compensated by ocean mass trend. To further analyze this feature, we compared our thermosteric height estimation with thermosteric height produced by NOAA (https://www. nodc. noaa. gov/OC5/3M_HEAT_CONTENT/tsl_global. html) finding a slightly higher trend (not shown). We also used reanalysis data from Mercator (http://marine. copernicus. eu/) to estimate the steric height, thermosteric and halosteric components in this box from the surface to 2000 m for the period 2005-2016. The results showed a positive steric height trend (1. 76 ± 1. 31 mm/yr) as a combination of temperature and salinity changes (not shown). Even though Argo underestimated the steric height trend, it did capture the importance of the halosteric height in the area (Figure  8df ).
Discussion and Conclusions In this work, we studied the South Atlantic mean sea level (SAMSL) trend derived from two satellite altimetry products (CMEMS and CSIRO) and the role of the main drivers responsible for the global mean sea level rise: ocean mass change and steric height. Results indicate that the SAMSL trend is 2. 56 mm/yr using CMEMS data and 2. 21 mm/yr using CSIRO data. The trend in ocean mass change (sum of the mass change in the Antarctic, Arctic and glaciers) detected by GRACE in the region is 2. 22 mm/yr. We then concluded that the SAMSL trend is dominated by ocean mass changes. The contribution of steric height, estimated with gridded Argo density data for the period of time coincident with the GRACE measurements, is smaller than the mass change contribution. This is in agreement with global mean sea level trend studies (e.
On one hand, the changes in circulation are associated with density changes (steric height), and to wind changes. For instance, a spin-up of the subtropical gyres in the southern oceans has been detected and associated with an increase in the Southern Hemisphere Annular Mode by analyzing sea surface height trends with satellite and Argo data (e. g. ,  [17, 62] ). On the other hand, the anticyclonic and cyclonic eddies might increase or decrease the sea level locally, and more importantly, the heat transport by eddies would affect the air-sea interaction. The one-degree spatial resolution of CSIRO helps to filter out the mesoscale activity. We thus suggest that part of the differences observed between the CSIRO and CMEMS trends are due to the eddy activity. Our results also show that CMEMS and CSIRO SLA trends are more similar when the longest matching period is considered, suggesting that the influence of mesoscale in the SLA trends decreases when considering long periods.
Acknowledgments: We want to thank the two reviewers and the editor for their useful comments. GRACE ocean data were processed by  Don P. Chambers , supported by the  NASA   MEaSUREs Program , and are available at http://grace. jpl. nasa. gov. The satellite altimetry data and reanalysis model from Mercator are available at Copernicus Marine Environment Monitoring Service (CMEMS; https://marine. copernicus. eu/).