dataesr/scientific-paragraphs-categorization huggingface dataset:
However, it remains difficult to draw any conclusions about the best method that could estimate Sargassum occurrence as far as the acquisition of in situ data is lacking to validate the retrieved products. The IFREMER CERSAT Global Blended Mean Wind Fields is a database that is available from the Copernicus services CMEMS, which provides 6-hourly wind components including wind speed between 1992 and 2020  [38]. The spatial means of the wind provided for the area of interest are 5. 5 m s -1 for 14 September 2020, and also for 28 December 2020; 6. 5 m s -1 for 8 July 2017; and more than 7. 2 m s -1 for 14 June 2020, and 27 May 2018. The scenes for which the wind speed is the highest (>6. 5 m. s -1 ) corresponds to the scenes where a higher coverage is estimated between 2 m and 5 m (>39% here).
The wind The retrieval of FC and z values from the neural network and from δMCI (only FC for this latter index) has also been applied to other scenes of the dataset (Section 2. 2). Table  3  summarizes the fractional coverage retrieved by both δMCI and neural network as well as the proportion of the coverage retrieved by the neural network between 2 m and 5 m depth. The coverage estimated using the neural network is higher by a factor of 2 than that derived using δMCI approach for all the scenes except for the scene acquired on 2 May 2021, for which the coverage is fairly similar for both methods. The relative increase in the coverage retrieval ranges from 0. 7% to 68. 6% for the scenes considered here when using the neural network in comparison to the current δMCI approach. The proportion of coverage estimated by the neural network between 2 m and 5 m ranges from 30% to 51%.
The inversion is carried out using an optimization process between the OLCI derived surface reflectance and the simulated surface reflectance from the forward version of the SRT model. The performances of the SRT inversion retrieval were first estimated using a simulated dataset since in situ data were not available. Typically, the RMSE values were lower than 1% for FC and about 0. 54 m for z, which is satisfactory. The proposed approach was then applied to Sentinel-3/OLCI sensor data. A relative increase of 31% in Sargassum coverage was found when comparing to the coverage that is currently estimated using the MCI algal index. It was shown that the aggregations are located deeper in the water column at the edge of their spatial area while their fractional coverage (or abundance) is weaker.
The inversion process that relies on the SRT physical model is highly time consuming, typically about 10 days for a typical OLCI image (4090 × 4865 pixels); therefore, a neural network approach was used to make more operational the application of the proposed method to a large dataset. The neural network was trained using a synthetic dataset. Seven Sentinel-3/OLCI scenes that were acquired at various years and seasons were examined. The Sargassum coverage retrieved by the neural network was about two times higher than that retrieved using the MCI approach for six out of seven scenes. The retrieval of the Sargassum depth showed that between 30% and 51% of the estimated depth are located between 2 m and 5 m. The consideration of the depth is then a relevant parameter that should clearly be taken into account for the estimation of the Sargassum abundance in the open ocean.
One epoch is defined as one training cycle for which the dataset is processed by the algorithm. The variation of the MSE with the epoch number for each step of the learning process (training, test, and validation phase) is shown Figure 10b. A similar shape is observed for the training and the test phase. The epoch number for which MSE of the test phase is minimum points out the number of training cycle that is sufficient for stopping the learning process. Here, the value of this epoch number is 42. The learning process could lead to overfitting of a given neural network when MSE for the validation phase increases with respect to the epoch number prior to that of the MSE for the test phase. Since the MSE for both the test and validation phases starts to increase for the same epoch number, namely, 42 (Figure.
The role of surface ocean anomalies for the continental Northern Hemisphere snow cover is investigated, together with the interactions between snow cover and atmosphere. Four observational datasets and two large multi-model ensembles of atmosphere-only simulations are used, with prescribed sea surface temperature (SST) and sea ice concentration (SIC). A first ensemble uses observed interannually varying SST and SIC conditions for 1979-2014, while a second ensemble is identical except for SIC where a repeated climatological cycle is used. SST and external forcing typically explain 10 to 25% of the snow cover variance in model simulations, with a dominant forcing from the tropical and North Pacific SST, while no robust influence of the SIC is found. In observations, the Ural blocking is the main driver of the November and April snow.
Observations Several snow datasets have been used to sample some of the observational uncertainty  (Brown et al. , 2010). For this study we use the monthly snow depth and snow cover of  ERA5-land (C3S, 2019) , resulting from the ECMWF land-surface H-TESSEL model forced with ERA5 atmospheric reanalysis  (Hersbach et al. , 2020)  for 1981-2014. We also use the monthly snow diagnostics from MERRA2  (GMAO, 2015; Gelaro et al. , 2017)  for the same period. The NOAA climate data record (CDR) of Northern Hemisphere weekly snow cover extent dataset  (Robinson et al. , 2021)  retrieved from the National Center for Environmental Information is aggregated into monthly time series in 1981-2014. We also use the monthly GlobSnow v3  (Pulliainen et al. , 2020 (Pulliainen et al. , ) snow depth in 1980 (Pulliainen et al.
After removing a linear trend from all the time series (not shown), the correlations with PC1BC that largely stem from SST forcing are smaller, and they only remain significant with ERA5-Land PC1 in November (R=0. 36), and with ERA5-Land (R=0. 47) and NOAA-CDR (R=0. 48) PC1 in April. This confirms the larger effect of changing SST and external forcing in November and April, although the EOF analysis in not robust among the observational dataset. There is no significant correlation in January, presumably because the internal variability is larger. The snow cover EOFs solely related to the sea ice are calculated from the MMM difference of ALL minus NoSIC (Figs. 8b, e, h ). The variance fraction explained by the first EOFSIC is between 10% and 13%. The absolute variance explained by EOFSIC (not shown) is 6 to 13 times smaller than the one explained by EOF1BC, which confirms that sea ice has a much smaller influence than SST or external forcing.
Internal variability of snow cover The impact of internal atmospheric variability on snow cover anomalies is investigated over Eurasia. We focus on Eurasia as it is the largest continent, and snow cover was previously suggested to influence the Northern Hemisphere atmospheric circulation  (Cohen and Entekhabi, 1999; Cohen et al. 2014; Gastineau et al. 2017 ). In the observational datasets, we remove a quadratic trend from all variables, which should remove a large part of the changes linked to the long-term evolution of external forcing, and then quantifies the internal variability, which is largely due to atmosphere-land processes, in addition to a residual influence of SST and SIC. The first EOF of the detrended snow cover in ERA5-Land is shown in Figs. 10a, 10e and 10i. The patterns are somewhat similar to those obtained over the Northern Hemisphere before detrending (Fig.
7 ), but the EOF1 over Eurasia in November and April corresponds to the EOF2 of the Northern Hemisphere before detrending. The regressions of the SLP and surface air temperature (Figs. 10b,f,j) onto the PC1 at no lag illustrate in January the influence of the NAO on the snow cover with cold air advection increasing the European snow cover. In November and April, the snow cover is linked to a trough over the Ural region, with cold (warm) air advection over its western (eastern) flank. The troughs over Central Eurasia are part of a wave-like perturbation between the Atlantic Ocean and Eurasia. In November, the trough extends to eastern Siberia and is associated with a warming and a snow cover decrease in Central Siberia. These patterns are similar in January when using the other three snow cover datasets (see Figs.
Role of the stratosphere for the January snow cover variability To understand the mechanism behind the statistical relationship between the January snow cover and the atmosphere, we focus on LMDZOR6 (the #5 model in Figs. 11g-i, 12g-i, and 13g-i. ), as daily outputs and three-dimensional atmospheric fields are available. LMDZOR6 reproduces the links between the January snow cover EOF1Int and the atmosphere one or two months later as shown in Fig. 13 , with nearly identical regression maps onto PC1Int when using only LMDZOR6 data (not shown). Figure  15  shows the lag regression of the daily polar cap temperature (north of 60°N) onto the PC1Int of the January snow cover. A significant lower-stratosphere warming is simulated from November to March. At 50-hPa, the temperature anomaly increases from 0.
Discussion and conclusion The land snow over the Northern Hemisphere is investigated in four observational datasets and in two large multi-model ensembles of atmosphere-only experiments, one with prescribed SST, sea ice and external forcing during the 1979-2014 period, and the other in which sea ice variations are replaced by their climatology. Although models have biases in representing the snow cover, the observed snow cover trend is overall well reproduced. In the multi-model ensemble mean, the trend is mainly driven by the external forcings and the associated SST warming, while sea ice loss drives a small and insignificant fraction of snow cover anomalies. Cohen et al. (2014) , among others, proposed that snow anomalies might amplify or damp the mid-latitude atmospheric circulation response to sea ice loss, but in our experiments, the SIC has little influence in driving the snow cover.
Acknowledgments https://doi. org/10. 5194/egusphere-2022-939 Preprint. Discussion started: 15 November 2022 c Author(s) 2022. CC BY 4. 0 License. We acknowledge support by the  Blue-Action Project  (European Union's  Horizon 2020 research and innovation programme , # 727852 , http://www. blue-action. eu/index. php?id=3498). The  LOCEAN-IPSL  group was granted access to the HPC resources of TGCC under the allocation  A5-017403  and  A7-017403  made by  GENCI. GG and CF were funded by the  JPI climate/JPI Ocean   ROADMAP  project ( ANR-19-JPOC-003 ). E. M. and D. M. acknowledge the support of the  German Federal Ministry of Education and Research  through the  JPI Climate/JPI Oceans NextG-Climate Science-ROADMAP  (FKZ:  01LP2002A ) Project. The NorESM2-CAM6 simulations were performed on resources provided by  UNINETT Sigma2 -the National Infrastructure for High Performance Computing and Data Storage in Norway  (nn2343k,  NS9015K ).
Drought legacy at a given sampling time was calculated from the measured emission value and the median of the emission values measured in the "only second D-RW" treatment at the considered sampling time as following: We used the median rather than the mean, because there was a low number of replicates (N = 8) for each sampling time and the data showed non-normal distribution. Drought legacy effects were calculated separately for CH 4 and CO 2 emissions. We constructed a full linear mixed model for each gas, with the drought legacy as the response variable, and the rst drought duration (factorial, 3 levels), the rst rewetting duration (factorial, 3 levels), the sampling time (continuous: 2, 4, 7 or 14 days) and their twoway interactions as xed effects. The storage time (continuous) and the sediments weight (continuous) were included as covariables, and the microcosm identity was included as a random effect.
Acknowledgements We thank  Leandro Sabagh  and  Robin Le Balle  for their help with the sediment samplings. We thank  Aline Gomes Dias Pinto Monteiro  and  Carlos Vitor Dourado Batista  for their help with the laboratory analyses. C. B. is grateful to the  Brazilian Coordination of Superior Level Staff Improvement (CAPES)  for a PhD scholarship. V. F. F. is partially supported by a  CNPq  ( National Council for Scienti c and Technological Development)  productivity grant (Process  310119/2018-9 ).
The integration time step was set as 30 minutes and H s , T m-01 , and θ m output data were recorded every 3 hours in each domain grid cell. H s and T m-01 were validated through a comparison with in situ wave data measured by the NOAA-42058 buoy, which is located in the central Caribbean Sea (Figure2). The wave reanalysis we use in this work is published at  [Orejarena-Rondón et al. , 2021a].
Wave model validation Validation of SWAN simulation is performed with the NOAA buoy 42058, which is located in the central Caribbean Sea, using both H s and T m-01 (see Figure  1 ). Conversely, due to the unavailability of other parameters, only modeled H s can be validated with those buoys located near of San Andres Island: buoy SAI Dimar and NOAA buoy 42059, which are located at the west and east of the basin, respectively (see Figure  1 ). In the case of the buoy NOAA-42058, a good fit between in-situ and model data was obtained. For H s , bias, correlation and Willmot coefficients values of 0. 064, 0. 83, and 0. 9 were obtained respectively, while for T m-01 , bias, correlation, and Willmot coefficients have values of 0. 089, 0. 74 and 0. 77, respectively (Figure  2 ). For the other two buoys, a good fit was found between modeled H s and in-situ H s estimates.
Temporal patterns of WEF over the Caribbean Sea from SOM analysis To better explore the dominant direction of WEF, we have applied a temporal SOM analysis over zonal (F x ) and meridional (F y ) components monthly mean WEF. As we use 3 × 3 neurons again, this analysis also provides 9 zones of covariability in the Caribbean Sea (Figure  8 ). Figure  9  shows time series of F x and F y components for every region in top and bottom panels, respectively. The sign (+/-) in the abscissas indicates the direction of each components: a negative value indicates a direction towards the west in the zonal component and towards the south in the meridional component, whereas a positive value indicates a direction towards the east in the zonal component and towards the north in the meridional one. Most of time series conserve the same sign, which allows to clearly identify the dominant direction.
Discussion Departing from a 60-year wave simulation we have analyzed the spatial and temporal variability of the mean wave energy flux (WEF). First, we have validated the simulated significant wave height (H s ) and mean wave period (T m-01 ), against in-situ data from buoy 42058 of NOAA (Figure  2 ). Correlation, Weibull and Bias coefficients shown a good agreement between modeled and observed data. The database here used contributes to reduce the lack of available information on wave parameters in the Caribbean Sea  [Orejarena-Rondón et al. , 2021a]. Indeed this simulation is the longest in comparison to other wave reanalysis available in the Caribbean Sea  [Arias et al. , 2009 , Dagua et al. , 2013 , Appendini et al. , 2015 , Osorio et al. , 2016] , and has been employed for marine energy analysis  [Orejarena-Rondón et al.
, 2022] , but can also we used for studies of coastal vulnerability, to analyze climate variability, or to study open sea extreme wave conditions for coastal and offshore engineering, among other. Data is freely accessible in the repository https://nimbus. imedea. uib-csic. es/s/JEaPEeeQNLPFJ3S [Orejarena-Rondón et al. , 2021b]. By applying a spatial SOM analysis over monthly mean WEF data we have regionalized WEF variability in the Caribbean Sea. Thus, wave information was classified into 9 patterns that collect the complex behavior of WEF (magnitude.
All regions were correlated with the ONI for periods between 24 and 40 months during years  1964-1967, 1972-1974 and 1978-1983, characterized  by the occurrence of large warm events in the eastern Pacific. This correlation occurs with the same phase in all periods, being larger between zones 4-9 (see Figure  13 ). Similarly, a strong correlation during years 1993-2017 is observed in all regions for a period that spans from 24 to 92 months, especially in zones 1, 4 and 7. CIENCIAS (Call 727) and POGO-SCORE Visiting fellowship at the Mediterranean Institute for Advanced Studies. Aurthors acknwoledge DIMAR (Dirección General Maritima from Colombia) for the wave parameters supplied for the model validation. J. M. Sayol thanks the joint funding received from the Generalitat Valenciana and the European Social Fund under grant APOSTD/2020/254.
Due to field constraints, paternal identity was determined genetically for 15 infants. For the remaining 65 infants with unknown paternity, we differentiated pairs of infants conceived during the tenure of the same alpha male from those conceived under tenures of different alpha males. Indeed, reproductive skew in mandrills is high, with alpha males monopolizing 60-70% of all conceptions  17, 18. While infants born during the tenures of "different alpha males" are thus most likely not paternally related, those born during the tenure of the "same alpha male" presumably include a large proportion of PHS. Using a linear mixed model (LMM) on this large dataset (N=2,616 dyads), and controlling for infant age and sex as well as maternal identity, we found that infants born during the tenure of the same alpha male or that were sired by the same genetic father resemble each other significantly more (lower average facial distance; Figure  1 , Table  1 ) than those born during the tenures of two different alpha males or to two different fathers (higher average facial distance).
The driving role of mothers Next, we show that mothers guide their offspring's social opportunities towards similar-looking infants and that this effect is robust while several other predictors of offspring associations are simultaneously controlled for. We restricted our image data set to those infant dyads for which we had detailed records on spatial associations routinely collected during behavioral observations (N=48 infants and their 30 mothers). Using generalized linear mixed models (GLMM with negative binomial family for over-dispersed count data), we analyzed spatial associations i) among infants ("infant-infant", N = 282 dyads); ii) between infants and other mothers ("mother-infant", N = 580); and iii) among mothers having infants at the same time ("mother-mother", N = 325) as a function of the residuals (see Materials and Methods) of facial distance among infants, and controlling for matrilineal origin, maternal relatedness and rank and infant age and sex.
During daily observations, we record detailed data on group composition and behavior. In addition, we take pictures of unambiguously identified individuals when visible and close (less than 10m away). In this study, we considered a total of 80 infants (39 females, 41 males; aged 4-365 days) who contributed to the dataset of portrait images and 48 infants (24 females, 24 males) born from 30 different mothers contributing to the study of spatial associations as a function of facial resemblance (excluding those infants that were aged more than a year apart or those for whom no behavioral data was available). Dates of birth were exactly known for 31 infants and estimated within a time-window of one day to two months for the remaining 49 infants based on patterns of mother's sexual swellings and infants' physical appearance.
Image database and pre-processing The mandrill face database includes ~16k images representing 276 different mandrills originating from the study natural population (12. 9k images), a semi-captive population housed at CIRMF (Centre International de Recherches Médicales de Franceville, Gabon; 2. 7k images) and other sources (the Internet, the Wildlife Reserves of Singapore, Zoo of Grandy; 0. 4k images). Images from Gabon (natural and CIRMF populations) were taken between 2012 and 2018. Pictures represent individuals that are awake and passive, awake and active (i. e. feeding, grooming, vocalizing) or anesthetized during several captures that occurred between 2012 and 2015 (representing 1. 1k images). We frequently photographed active individuals using the slow burst mode of cameras, which allows to capture variation in face position and expression while avoiding identical frames.
The multiple frames obtained when using the slow burst mode are hereafter referred to as a "burst-mode series". Images were then manually oriented to align pupils horizontally, and cropped to generate square portraits centered on the nose and excluding the ears. For training the DNN for both face identification and face verification, we used a learning dataset of 12. 2k pictures representing all individuals of the mandrill face database >1 yr, plus infants ≤1 yr not belonging to the 80 studied infants. Based on preliminary tests described previously  17  , we included in the learning set all pictures displaying a face in frontal view (approx. <30°) and with less than 50% of occlusion, of whatever quality provided that field assistants individually recognize the animal. Because the face of a given individual varies considerably between its different age classes (infant, juvenile, adolescent -only for males-, subadult -only for males-and adult), we used "ind-age" classes rather than individuals for the identification task, that is, we treated two ind-age classes representing the same individual as distinct and independent classes.
The final learning dataset contains 168 indage classes. This learning set was split further into a training set and a validation set. The validation set was used to parameterize the model for the face identification task. It contained two images of each class. For a correct evaluation of training performances, we ensured that none of the validation image was from a burst-mode series that also contained images present in the training set. The training set contained all other images of the learning set. Because we were able to reach high performances despite a large imbalance between classes in the training sets, we did not attempt to correct for this imbalance. The test dataset includes images of infants (≤1 yr) from the study natural population. To maximize the quality of dissimilarity measurements, we selected only the best-quality pictures: face in frontal view, sharp image, neutral light, occlusion limited to below the mouth, neutral facial expression, no shadow or lighting spot, single image of a burst-mode series.
Face identification We trained a DNN to identify ind-age classes as a goal to learn a deep representation of mandrill faces. We used the popular VGG-Face 54 as a starting point and retrained this network with mandrill portraits. This procedure, called "transfer learning", allows to reach high model performance even with relatively small datasets  55. VGG-face is a VGG16 that previously learned to recognize 2. 6k different humans from a total of 2. 6M portrait pictures. We replaced the last softmax classification layer of VGG-Face by a new layer of 168 dimensions (which corresponds to the number of classes in the new mandrill identification task). We included two dropout layers (with 50% dropout probability), one after each fully connected layer, to limit the risk of overfitting. We trained the network using a stochastic gradient descent with momentum optimizer, with initial learning rate of 10 -5 for all convolutional layers except for the two fully connected layers and the classification layer, for which the learning rate was set to 10 -3.