dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis of activity-related energy expenditure To determine the effect of age, sex, and their interaction on the amount of energy expended while flying, resting on the water, diving and taking off, we ran four separate General Linear Models with a Gaussian distribution using backwards stepwise selection in the MASS Package  (Venables and Ripley 2002)  in R version 3. 5. 3 (R Core Team 2017). The full set of explanatory variables used in our models were age (continuous), age 2 (continuous), and sex (categorical), with an interaction between age and sex, and age 2 and sex. A backward stepwise selection  (Zhuang et al. 2018 ) was applied to identify the best subset model (with and without interactions). We examined the effect of age and sex on (1) total energy expended, and (2) energy expenditure of each of the four at-sea activities.
Acknowledgements We thank  Tiaan Strydom ,  Manon Amiguet ,  Laëtitia Vibert ,  Bénédicte Martin  and  Alexei Dyer  for their time and effort assisting with the deployment of accelerometers and GPS devices on known-age Cape gannets on Malgas Island. This work is based on the research supported by the  National Research Foundation of South Africa  (NRF Grant Number:  MND210426597498 ), The  French National Centre for Scientific Research (CNRS) ,  Ernst and Ethel Eriksen Trust , The  REHABS Lab , The  Wildlife Ecology Lab  and the  Nelson Mandela University Post-Graduate Research Scholarships.
Funding Open access funding provided by Nelson Mandela University. This work is based on the research supported by the  National Research Foundation of South Africa  (NRF Grant Number:  MND210426597498 ), The  French National Centre for Scientific Research (CNRS) ,  Ernst and Ethel Eriksen Trust , The  REHABS Lab , The  Wildlife Ecology Lab  and the  Nelson Mandela University Post-Graduate Research Scholarships.
Data fitting method The FFG and HDB models defined by Eqs. (1) and (2) were fitted to published experimental data by minimizing the sum of squared residuals (SSR) as described in Eq. (8) , where qi,obs represents the experimental value of q, qi,fit denotes the value of q calculated from the chosen isotherm model, and m is the number of data points. Engauge Digitizer (https://markummitchell. github. io/engauge-digitizer/) was used to extract data points from the original sources. ( ) 2 , , 1 SSR m i obs i fit i q q = = - ∑ (8) It should be noted that Eqs. (  1 ) and (  2 ) are implicit in q, indicating that there is no closed algebraic expression of q in terms of the model parameters and c. To obtain qi,fit from Eq. (  1 ) or (2), a numerical approach using Newton's method was integrated into a nonlinear leastsquares fitting procedure implemented in Mathematica.
Panels a and b of Fig. 1  present the S-shaped isotherm illustrating the adsorption of auramine-O on a modified cellulose adsorbent at pH 7, as extracted from the study by Teodoro et al. [19]. In their study, Teodoro et al. [19]  fitted the FFG and HDB models to the experimental isotherm using a nonlinear regression procedure implemented in MATLAB. The parameter estimates reported by Teodoro et al. [19]  are reproduced in Table  1. In our study, we used a nonlinear least-squares regression procedure coded in Mathematica to fit the FFG and HDB models to the auramine-O adsorption data. Panels a and b of Fig. 1  display the fits generated by the two models. Our parameter estimates presented in Table  1  show excellent agreement with those reported in the original study, thereby validating the accuracy of our data fitting procedure.
However, ambiguous definitions, unclear MRV techniques that may differ from country to country, and scarce technical and financial resources in many developing countries limit the reliability and credibility of current approaches  3, [9] [10] [11]. Moreover, defining better classes to separate large trees, with high ecological and economic value, from shrubs would be an important improvement over current definitions. Similarly, for closed canopies the classification of forests by canopy height is desirable, both to ensure that small shrubs are not mapped as tree cover, and to differentiate between primary, secondary, or plantation forests. This requires the incorporation of canopy height datasets, such as satellite, aeroplane-, or UAV-based lidar measurements. Overall, the principal problem remains the consistent assessment of all tree and forest resources, across both countries and years.
A continentally consistent high resolution tree cover product is thus an important step to comparing and harmonizing the different forest definitions used across countries and institutions. As part of this study, tools were developed to automate the process of generating uniform mosaics of PlanetScope scenes for any grid tile globally, and for predicting tree cover for a specific year or date range. Given that PlanetScope imagery is available every day for the whole world, these processing methods lay the groundwork for global tree cover mapping at annual scale and very high spatial resolution. While this would require further annotation data to cover new ecosystems, the labelling effort is expected to be lower due to the variety of ecosystems already trained for the Africa model. Additionally, the free availability of NICFI PlanetScope data for the tropics makes this methodology feasible for developing countries at national level.
Alternative ways to produce reliable training data are an ongoing topic of research  34  , and it is likely that the automatic integration of auxiliary very highresolution data such as UAV imagery or LiDAR will play an increasingly important role in creating training data. Furthermore, the map provided here is a prototype and there are several limitations in the use of PlanetScope data that require future research. First, the detection of small, isolated trees with crown sizes below 30 m 2 is challenging, which hinders the applicability of our approach to monitoring newly planted trees, natural regeneration, and bushes. Second, only isolated trees can be reliably identified as individuals. We also need to explore the feasibility of splitting closed-canopy predictions into single trees, which would improve the estimation of carbon stocks by enabling the use of allometric models based on single tree crowns  16, 35.
Third, current tree height information is not available at a high spatial resolution, so the applied separation of forest types is based on the relatively coarse GEDI LiDAR data. Moreover, while comparisons with LiDAR data showed that we can roughly state that trees and shrubs above 5 m are detected, this number should not be considered as a hard threshold but more as an orientation. It cannot always be guaranteed that bushes and smaller shrubs are not partially mapped, and also there is no strict guarantee that all trees above 5 m are captured. Future versions of this map may extend the use of LiDAR data to training the model, leaving less uncertainty regarding the definition of the mapped trees. Fourth, from the original spatial resolution of the images, it is not possible to detect gaps between dense tree crowns and forest canopies, leading to overestimations in some forests and shrublands.
This effect is aggravated by understory that fills gaps between larger trees, for example in woodlands. Fifth, PlanetScope images are not available before 2015. The possibility of using our data to calibrate Landsat time series to assess changes over longer times needs to be explored. Tree cover maps generated from current nanosatellites do not have the level of precision of sensors that record images at sub-metre resolution, so there is a trade-off between precision and cost, scalability, and repeatability, although ongoing improvements to the current product will reduce many of the above-mentioned limitations. Our prototype maps represent a combination of continental scale and single tree precision that sets the stage for a new era of global tree cover mapping that has the potential to overcome forest definition ambiguity.
Satellite images We used high-resolution satellite images from the PlanetScope constellation of nanosatellites, with all data obtained from Planet Labs through a research license. The images consist of 4-band multispectral scenes at 3 m resolution, with atmospherically corrected surface reflectance values for the Blue, Red, Green, and Near-infra-red bands, and were available via the Planet API with the PSScene analytic_sr product bundle  39. For the continental study area, we downloaded about 230,000 individual PlanetScope scenes from 2019, covering a total area of 24,222,164 km 2.
The criteria used to select scenes include the date and instrument type, as well as metadata properties on quality such as the amount of clouds, haze, shadow, 'clear confidence', and a general quality indicator. For tiles where there were not enough scenes due to frequent cloud cover, the date range was automatically progressively extended forwards and backwards in time until a coverage threshold of 99% was achieved. We did not perform any temporal merging of the 3 m PlanetScope scenes to create composite images, because small differences in view angle and orthorectification of subsequent acquisitions introduce significant noise at the scale of small trees. The selected scenes per tile were clipped to their partial footprints, downloaded in parallel, reprojected to WGS84, and merged into a mosaic.
Evaluation of tree cover results with LiDAR data To evaluate the accuracy of the model's predictions of tree cover, we compared our binary tree cover map with canopy height maps (CHMs) derived from aerial LiDAR and stereo UAV imagery. We did not use manual labels because these are not independent and are impacted by the choice of the person doing the labelling. The LiDAR and UAV stereo photography data cover different landscape types, such as savannah, shrubland, woodland, rainforest, and cropland, and were available for sample sites in Senegal (3 cm and 70 cm resolution; 4. 6 km 2 area), DRC (1 × 1 m; 79. 6 km 2 area), and Mozambique (1 × 1 m; 68. 5 km 2 area). Using the resulting CHM images, we sampled 400 random plots of 50 ha each for DRC and Mozambique and used the 59 sample plots of 2-15 ha from Senegal.
We then compared the percent tree cover per plot with PlanetScope tree cover over the same area. Because the CHM data are based on tree height, a certain minimum height had to be chosen as a cutoff to define a tree crown, in order to compare to tree cover at the plot level. We used different LiDAR minimum height thresholds ranging from 3 m to 7 m. While there were country-specific variations, the best correlation with the CHM was found with a 5-6 m threshold, indicating that the PlanetScope tree cover maps trees above approximately 5-6 m (Supplementary Fig. 6a ). Using a 6 m threshold, we then obtained an R 2 = 0. 81 and RMSE = 16. 2% for Senegal, R 2 = 0. 62 and RMSE = 5. 2% for Mozambique, and R 2 = 0. 93 and RMSE = 10. 0% for DRC. The overall RMSE across all countries was 9. 19% for a height threshold of 6 m, and 9.
To limit the effect of these differences, we selected 50 cm sample scenes from their dataset that were from later than 2015, in a similar month as the PlanetScope mosaic, and where automatic image alignment was successful. Furthermore, we restricted the scenes to croplands, as the PlanetScope model does not map trees as individuals if tree cover is dense. We then compared the number of 50 cm mapped trees that were detected by the PlanetScope model, where a 50 cm tree is considered successfully detected when it overlaps with any PlanetScope tree, to account for occasional clustering of trees in the PlanetScope model (Supplementary Fig. 7 ). Note that Supplementary Fig. 7a  shows a 'best-case' scenario of an area in Nigeria, where the Planet imagery is clear and there is minimal shift. It can be concluded that detections become reliable at 30 m² crown size or larger, for smaller crown sizes, about half of the trees are missed by our model.
Environmental data and auxiliary datasets We used land cover data from both the ESA WorldCover 2020 product at 10 m 18 and the Global Land Cover and Land Use Change product at 30 m 47 to mask out water bodies at prediction time. The WorldCover map was also used to mask croplands for the analysis of trees on croplands. We used historical rainfall data from the Climate Hazards Group Infra-red Precipitation with Station (CHIRPS) product to generate a reference rainfall map  23. We averaged the annual rainfall data from 1982 to 2018 and bilinearly interpolated to 100 m for analysis with tree cover. We further used tree cover from Hansen et al. 12  , where the tree cover for 2019 was produced by starting with the tree cover in 2000 and adding the available annual forest gain up to 2013, then subtracting annual forest losses up to 2019.
Acknowledgements We thank  Norway's International Climate and Forest Initiative (NICFI)  satellite data Level 2 programme for providing parts of the very high-resolution satellite imagery for the study. This work was funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Programme  (grant agreement no. 947757 TOFDRY). R. F. , C. I. , and A. K. acknowledge support from  Villum Fonden  through the project  Deep Learning  and  Remote Sensing  for  Unlocking Global Ecosystem Resource Dynamics (DeReEco). A. D. , P. B. , and J. S. acknowledge support from  Karingani Holdings  for funding the LiDAR data collection and processing for Mozambique. C. I. acknowledges support by the  Pioneer Centre for AI, DNRF  grant number  P1. J. C. acknowledges grants from  Investissements d'Avenir (CEBA, ref.
ANR-10-LABX-25-01 ;  TULIP, ref. ANR-10-LABX-0041 ),  ESA CCI-Biomass  and  CNES. D. S. acknowledges support by  NASA , grant no. 80NSSC21K0315  from the  Land Cover and Land Use Change Program. M. M. was supported by a  DFF Sapere Aude  grant (no. 9064-00049B ).
Data availability Planetscope imagery was partly from a departmental research license and partly from Norway's International Climate and Forest Initiative (NICFI) satellite data Level 2 programme. NICFI Planetscope imagery in tropical areas is available for non-commercial purposes from Planet Labs at https://www. planet. com/nicfi/. However, we did not use the basemaps provided in the frame of the NICFI programme but generated our own mosaics from the raw data. The derived tree cover maps produced in this study have been deposited in the Zenodo database and are available at https://doi. org/10. 5281/zenodo. 7764460  48. Note that the maps are under active development and improved versions will be released in the future. The ESA WorldCover product  18  used for the land cover analyses is available for download at https://doi.
org/10. 5281/zenodo. 5571936. The global canopy height data from Lang et al. 21 are available for download at https://share. phys. ethz. ch/~pf/nlangdata/ETH_GlobalCanopyHeight_10m_2020_ version1/. The global canopy height data from Potapov et al. 22  are available for download at https://glad. umd. edu/dataset/gedi. The global tree cover data from Hansen et al. 12  are available for download at https://storage. googleapis. com/earthenginepartners-hansen/ GFC-2021-v1. 9/download. html.
Code availability The code for the tree detection framework based on U-Net is publicly available at https://doi. org/10. 5281/zenodo. 3978185. Support and more information are available from A. K. (ak@di. ku. dk or ankit. ky@gmail. com) or F. R. (flr@ign. ku. dk). Code for preparation of imagery from PlanetScope raw scenes has been deposited in the Zenodo database and is available at https://doi. org/10. 5281/zenodo. 7764360. Support and more information are available from F. R. (flr@ign. ku. dk). The python libraries used in the image preparation and prediction pipelines are publicly available and include GDAL v3. 1. 2, rasterio v1. 2, tensorflow v2. 5, geopandas v0. 9, and the Planet python API v1. 4. 7.
org/ (accessed on 15 February 2023)), the EVIDENCE project (2018-2023, http://www. evidence-ci. org/ (accessed on 15 February 2023), which aims to help to reduce the risks associated with extreme rainfall events impacting urban populations' living conditions, a unique opportunity has arisen to address the lack of measurement data. During these projects, 21 rain gauges have been strategically distributed across the urbanized area of the district to gather essential rainfall data. The aim of this study is to identify and characterize the various rainfall events that occurred in Abidjan's district from a densified network of rain gauges in the urban area using data collected from 2017 to 2022. This is one of the first networks of such density deployed in urban areas on the coast of the Gulf of Guinea.
5 m from the ground has a surface of 400 cm 2. The rain that has passed through the rain gauge is collected in a totalizing can at the foot of the apparatus. The tipping dates of the buckets (day, month, year, hour, minute, and second) are recorded in a HOBO Pendant ® data logger and then collected and reviewed monthly. After a critique and comparison with the totalizer, the tipping dates are aggregated in regular 5-min time steps. When a rain gauge malfunctions, its data are put into gaps over the identified period, and the values are replaced by NAN (not a number). In practice, the data we used in the present study are spread over four years from 7 January 2018 to 31 December 2021 to consider year from which we start densification of the network. In addition, before 2018, the network operation was not optimal, with many gaps at some stations.
The data are available in the ORIESA database and identified with the DOI: https://doi. org/10. 23708/JOE1X6? Thirteen (13) of these rain gauges are equipped with a remote transmission system. The data are processed and displayed on a map of the city, showing rainfall totals over the last 5 min up to the previous 3 days. The maps can be viewed in real time at http://www. babipluie. org/ (accessed on 20 February 2023) and are updated every 5 min. Considering the small spatial extension and the relative density of our network (1 rain gauge/60 km 2 on the urbanized part and 1 rain gauge/100 km 2 at the district scale), we adopted a rainfall event definition that is similar to Löwe et al. [24]. This definition is mainly based on the criteria of the intensity threshold, minimum inter-event duration (MIT), and rainfall event duration.
Therefore, the choice of an MIT of 30 min to distinguish two consecutive events appears suitable. It takes into account both the temporal resolution of our data and the climatic environment of the study area. In the district of Abidjan, rainfall events are dominated by thunderstorms and monsoon rains  [28]. These meteorological conditions can lead to consecutive rainfall events that differ in their meteorological characteristics. Our value of MIT is identical to the 30 min used by Kouamé  [29]  in the northwest of the Côte d'Ivoire under a Sudanian climate, and by D'amato and Lebel  [30]  over the square degree of Niamey in Niger under a Sahelian climate, which is drier. This study used the drawre function of the IETD library  [31]  of the R software version 3. 6. 2  [32] , which allows for the identification of the beginning, end, duration, average intensity, and rainfall amount of the different rainfall events from the average hyetograph.
duration, rain intensity, and amount (depth) of these events varied. By simultaneously applying the criteria of a minimum duration of 30 min and a minimum rainfall depth of 1 mm, observed in at least one rain gauge, the number of identified rainfall events is reduced by more than half from 2764 to 1240. Figure  3  shows the result of the identification of rainfall events between November 7 and 8, 2021 from the average hyetograph. It is evident that most of the events were identified. The events or time steps not associated with a rainfall event remain marginal in terms of both the quantity and duration of the rain. 3  above as measured by the rain gauge network. This event was not measured by all the rain gauges as some stations were dysfunctional and some data were missing. The maximum intensity (in 5 min) of this event exceeded 150 mm/h, but this peak of rain rate varied significantly over the network.
We use state-of-the-art automated morphometric and identification methods to collect a large and robust data set, and our new high-throughput workflow based on machine-learning techniques is validated by comparison with human counts. Using statistical modeling, we test the OSH, investigating whether species' optimum conditions are best inferred from their relative abundances or from the environmental parameters that mostly drive variations in their sizes (33 parameters are tested). We investigated the environmental drivers of size variations in TIO planktonic foraminifera at the species and assemblage levels. We focus on the TIO because a significant amount of data on planktonic foraminifera distribution in this region already exists, and because this region contains highly contrasted oceanic environments in terms of physico-chemical characteristics (oxygenation, stratification, PP, carbonate chemistry, salinity).
Environmental Data The environmental data for the ocean overlying each site were retrieved from oceanographic databases using mean annual values from different sources as reported in Table  1. Environmental parameters were selected to capture a wide variety of variables including (but not limited to) parameters that have been suggested to influence planktonic foraminifera size and assemblage distribution: temperature, nutrient concentrations, salinity, oxygen concentrations, carbonate system parameters, and thermal gradients  (Cayre et al. , 1999; Clemens et al. , 1996; Kuroyanagi et al. , 2013; Moller et al. , 2013; Schmidt, Renaud, et al. , 2004). Since the average living depth of most planktonic foraminifera is within the upper 200 m of the water column  (Lessa et al. , 2020) , most of the environmental data used in this work come from 0 to 200 m, so as to capture both surface and sub-surface conditions.
The environmental data sets that we used are archived in the French Open Access database, SEANOE, and can be accessed at: https://doi. org/10. 17882/86211 (Adebayo, Bolton, Marchant, Bassinot, Sandrine, et al. , 2022). The spatial variability in surface ocean parameters in the TIO was analyzed using a covariance matrix principal component analysis (PCA) to identify the surface hydrographic settings of our study area. The paleontological statistical software Past  (version 4. 01, Hammer et al. , 2001)  was used for this analysis. Following  Marchant et al. (2020) , splits of the >150 μm size fraction were transferred to MiSo and images of each particle (primarily/exclusively whole foraminifera or foraminiferal fragments) were acquired (total N = 311,380, average N per sample = 3,797). With the aid of ParticleTrieur, a software developed at CEREGE  (Marchant et al.
Morphometric parameters (area, perimeter, convex area, convex perimeter, major axis, minor axis, minimum enclosing circle area, minimum enclosing circle radius, eccentricity, solidity, roundness, circularity, mean, standard deviation, standard deviation invariant, skewness, kurtosis, fifth moment, and sixth moment) as well as the abundance of each species within each assemblage were extracted from ParticleTrieur. For more details on ParticleTrieur workflows, see  Marchant et al. (2020). The image data sets generated in this study using MiSo are archived in the SEANOE database and can be accessed at: https://doi. org/10. 17882/86411 (Adebayo, Bolton, Marchant, Bassinot, Conrod, et al. , 2022).