dataesr/scientific-paragraphs-categorization huggingface dataset:
The 2S-SOM algorithm is well adapted to this complex task by weighting the different inputs. The clustering algorithm was calibrated on a restricted database composed of remotely sensed observations collocated with measurements taken in the global ocean. In the present paper, we propose the retrieval of the major pigment concentrations from satellite ocean-color multispectral sensors in the Senegalo-Mauritanian upwelling, which is an oceanic region off the coast of West Africa where a strong seasonal upwelling occurs (Fig. 1 ). The Senegalo-Mauritanian upwelling is one of the most productive eastern boundary upwelling systems (EBUSs) with strong economic impacts on fisheries in Senegal and Mauritania. Since the region has been poorly surveyed in situ, we have chosen to extract pertinent biological information from ocean-color satellite measurements.
The calibration database (DPIG) The calibration database (DPIG) comprises in situ pigment measurements collocated with satellite ocean-color observations by the SeaWiFS (Sea-Viewing Wide Field-of-View Sensor). This DPIG is composed of 515 matched satellite observations and in situ measurements made in the global ocean (mainly in the North Atlantic and the equatorial ocean; Ben  Mustapha et al. , 2014). The matchup criteria were quite severe: we used satellite pixels situated at a distance of less than 20 km from the in situ measurement in a time window of ±12 h. The geographic distribution of the 515 coincident in situ and satellite measurements is shown in Fig. 2. The matchup procedure between in situ and satellite observations is a crucial question to estimate remote sensing algorithms. If the parameters of the procedure are too severe, the number of collocated data points dramatically decreases.
The in situ chl a concentration ranges between 0. 007 and 3 mg m -3 (see Table  1 ). The five Ra(λ) are defined following  Alvain et al. (2012) : Ra(λ) = ρ W (λ)/ρ Wref (λ, chl a), (2) where the parameter ρ Wref (λ, chl a) is an average reflectance depending on the chl a concentration only that was computed according to the procedure reported in  Farikou et al. (2015). Ra(λ) is a nondimensional parameter that depends on the chl a abundance at second order and is mainly sensitive to the secondary pigments  (Alvain et al. , 2012). The DPIG database thus provides information on the existing links between the pigment composition and the SeaWiFS measurements. The pigment composition is defined by the pigment ratios, which are nondimensional variables of the form in the present study: pigment ratio = DP/Tchl a, (3) which is defined as the ratio of the diagnostic pigment (DP) versus the total chl a (Tchl a = chl a+divinyl chl a), according to  Alvain et al.
The Senegalo-Mauritanian upwelling satellite data (DSAT) The satellite dataset we processed to retrieve the pigment concentration consists of five ρ w (λ) and five Ra(λ) at five wavelengths  (412, 443, 490, 510 and 555 nm) , as well as the SeaWiFS chl a concentration observed in the Senegalo-Mauritanian upwelling region (8-24 • N, 14-20 • W; Fig. 3 ) during 11 years  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009)  by SeaWiFS. This dataset is denoted here as DSAT. The satellite observations (ρ w (λ) and chl a concentration) were provided by NASA with a resolution of 9 km. Due to the presence of Saharan dust in this region, very few estimations of satellite ρ w (λ) and in situ chl a were available, and some satellite estimations of chl a could present strong overestimations  (Gregg et al.
The UPSEN database Recently, some HPLC measurements were made in the Senegalo-Mauritanian region during two oceanographic cruises (UPSEN campaigns) of the oceanographic ship www. ocean-sci. net/16/513/2020/ Ocean Sci. , 16, 513-533, 2020   Le Suroit from 7 to 17 March 2012 and from 5 to 26 February 2013 as reported in  Ndoye et al. (2014)  and  Capet et al. (2017). The goal was to study the dynamics and the biological variability of the Senegalo-Mauritanian upwelling. During these campaigns, in situ HPLC measurements were carried out. We expected to be able to collocate them with the ocean-color VIIRS (Visible Infrared Imaging Radiometer Suite) sensor observations, whose wavelengths are close to those of the SeaWiFS. Unfortunately, we were only able to process satellite observations made on 21 February 2013 due to the presence of clouds and Saharan aerosols the other days.
The SOM clustering The SOM algorithms  (Kohonen, 2001)  constitute powerful nonlinear unsupervised classification methods. They are unsupervised neural classifiers that have been commonly used to solve environmental problems  (Cavazos, 2000; Hewitson and Crane, 2002; Richardson et al. , 2003; Liu and Weisberg, 2005; Liu et al. , 2006; Niang et al. , 2003 Niang et al. , , 2006;; Reusch et al. , 2007). The SOM aims at clustering vectors z i ∈ R N of a multidimensional database D. Clusters are represented by a fixed network of neurons (the SOM), each neuron c being associated with the so-called referent vector w c representing a cluster. The self-organizing maps are defined as an undirected graph, usually a rectangular grid of size p × q. This graph structure is used to define a discrete distance (denoted by δ) between two neurons of the p × q rectangular grid that presents the shortest path between two neurons.
Each vector z i of D is assigned to the neuron whose referent w c is the closest in the sense of the Euclidean distance: w c is called the projection of the vector z i on the map. A fundamental property of an SOM is the topological ordering provided at the end of the clustering phase: close neurons on the map represent data that are close in the data space. The estimation of the referent vectors w c of an SOM and the topological order is achieved through a minimization process in which the referent vectors w are estimated from a learning dataset (the DPIG database in the present case). The cost function is shown in Appendix A. The SOMs have frequently been used in the context of completing missing data  (Jouini et al. , 2013) , so the projected vectors z i may have missing components. Under these conditions, the distance between a vector z i ∈ D and the referent vectors w c of the map is the Euclidean distance that considers only the existing components (the truncated distance or TD hereinafter).
The calibration phase Similarly to the standard SOM, the 2S-SOM is determined through a learning phase by using a more complex cost function (see Appendix A) that estimates for each neuron, in addition to the referent vector, a weight (α) for each block. For a neuron c, we define the weights of each block b (b = 1. 4). At the end of the calibration phase, each element z i of the dataset DPIG is associated with a referent w c whose components are partitioned into four blocks. In the present study, the 2S-SOM is represented by a two-dimensional (9 × 18 = 162) grid that represents the partition of the DPIG dataset into different classes. Each class provided by the 2S-SOM is associated with a so-called referent vector w c with c ∈ {1. 162}. The size of the map has been determined by using the procedure provided by the SOM software available at http://www.
Statistical validation of the method The validation of the method was focused on the retrieval of the fucoxanthin ratio, which is a characteristic of diatoms, www. ocean-sci. net/16/513/2020/ Ocean Sci. , 16, 513-533, 2020 but the same procedure could be applied to any pigment. The hyper-parameter µ (see Appendix A) was optimized in order to retrieve that ratio, while η was set as constant since only the blocks were weighted in the present study. Due to the small amount of data in the DPIG, we estimated the accuracy of the fucoxanthin retrieval by a cross-validation procedure, which is a powerful procedure in statistics. The principle is the following: we learned 30 2S-SOMs using 30 different learning datasets L i constituting 90 % of DPIG taken at random, and then we computed a statistical estimator on the retrieved quantities using 30 test datasets (10 % of DPIG).
The algorithm was as follows. Starting with i = 1. 30: 1. determination at random of a learning dataset L i (90 % of DPIG) and a test dataset T L i (10 % of DPIG); 2. training of a 2S-SOM M i using L i (see Sect. 3. 2 and 3. 3); 3. validation using T L i according to the procedure described in Sect. 3. 4; and 4. estimation of the RMSE i and R 2 i on T L i between the estimated and observed fucoxanthin ratios. The flowchart of the cross-validation procedure is presented in Fig. 5  for the computation of the mean RMSE and R 2 (R 2 , RMSE = 1 30 I =30 i=1 R 2 i, RMSEi). Statistical parameters (R 2 coefficients, RMSE and P values) of the cross-validation between the DPIG in situ pigments and the pigments given by the 2S-SOM averaged for the 30 2S-SOM realizations, which are presented in Table  2 , show the good performance of the method.
The remaining 11 components (not shown) exhibit the same coherent topological order. One can observe a very good topological order for the fucoxanthin ratio that was favored by the determination of the hyper-parameter µ. Moreover, the bottom right region in the 2S-SOM (Fig. 6 ) may correspond to the diatoms with a good confidence since high fucoxanthin is associated with a high chlorophyll concentration and low peridinin. This is confirmed in Sect. 5 by looking at the geographical location of the different pigment concentrations  (Figs. 8, 10, 11). Another important remark is that the value of each component presents a large range of variation of the same order as the range of variation found in the DPIG variables. This means that the 2S-SOM has captured most of the variability of the dataset.
The pigment estimation from SeaWiFS observations in the Senegalo-Mauritanian upwelling region We decoded the DSAT database (Sect. 2. 3) using the 2S-SOM for 11 years  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009)  of SeaWiFS data observed in the Senegalo-Mauritanian upwelling region (8-24 • N, 14-20 • W). This study was done according to the retrieval phase described in Sect. 3. 4. For each day, we projected the 11 SeaWiFS observations (5ρ w (λ), 5Ra(λ) and chl a) of each pixel on the 2S-SOM. At the end of the assignment phase, each pixel of a satellite image was associated with six pigment concentration ratios. The underlying assumption is that the link between the remote sensing information and the pigment ratios of a pixel is provided by the selected referent w c.
5. 1). The 2S-SOM method gives pigment concentrations that are close to those obtained by in situ observations. The method could be applied to a large variety of other parameters in the context of studying and managing the planet Earth. The major constraint to obtaining accurate results is to deal with a learning dataset that statistically reflects all the situations encountered in the observations processed. Due to its construction, the method cannot be used to find values beyond the range of the learning dataset. Table  3. For ship stations 1, 2, 3, 5a and 5b of the UPSEN campaign, we show the referent captured by the VIIRS observations, the fucoxanthin ratio associated with this referent (Rfuco-2S-SOM), the fucoxanthin ratio of the closest DPIG fucoxanthin ratio captured by the neuron of the referent and the fucoxanthin ratio measured in situ during the UPSEN campaign.
Another suitable procedure was to embed NN in a variational inversion, which is a very efficient way when a direct model exists  (Jamet et al. , 2005; Brajard et al. , 2006a, b; Badran et al. , 2008). Statistical analysis of the absorption spectra of phytoplankton and pigment concentrations was conducted by  Chazottes et al. (2006 Chazottes et al. ( , 2007) )  using an SOM. In the present study, due to the fact that the learning dataset was quite small (515 elements), we used an unsupervised neural network classification method, which is an extension of the SOM method well adapted to dealing with a small database whose elements are very inhomogeneous. We clustered available satellite ocean-color reflectance at five wavelengths and their derived products, such as chlorophyll concentration and the associated in situ pigment ratios.
The major points of this study are as follows. 1. The clustering was carried out by developing a new neural classifier, the so-called 2S-SOM, which presents several advantages with respect to the classical SOM. As in the SOM, we defined clusters that assemble vectors that are close together in terms of a specified distance. This classifier was learned from a worldwide database (DPIG) whose vectors are ocean-color parameters observed by satellite multispectral sensors and associated pigment concentrations measured in situ. In the operational phase, SeaWiFS images are decoded, allowing for the estimation of the pigment concentration ratios. The major advantage of 2S-SOM with respect to the classical SOM is to cluster variables having similar physical significance into blocks having specific weights.
2. The 2S-SOM decomposes the DPIG into a large number of significant ocean-color classes, allowing for the reproduction of the different possible situations encountered in the dataset we analyze. We assume that the relationship between the pigment concentration and the remotely sensed ocean-color observations is independent of the location, which is justifiable since the relationship depends on the optical properties of ocean waters through well-defined physical laws that are regionindependent. This also supports the fact that we used a global database to retrieve pigments in a definite region. In contrast, the different phytoplankton species vary from one region to another, making the relationship between the pigment ratio and phytoplankton species strongly dependent on the region. This justifies the fact we focused our study on the pigment retrieval rather than on the PSC or PFT, as mentioned above.
We found evidence of a large offshore gradient of fucoxanthin concentrations, the nearshore waters being richer than the offshore ones. We showed that the offshore region waters correspond to case 1 waters, while the nearshore waters are close to case 2 waters and are influenced by the variability of nearshore process like turbidity or the presence of dissolved matter. The UP-SEN measurements show that the pigment ratios of the Senegalo-Mauritanian region are in the range of the DPIG database used to calibrate the method, which justifies the use of the 2S-SOM algorithm to investigate this region. 4. We used daily satellite observations to construct a monthly climatology of pigment concentrations of the Senegalo-Mauritanian upwelling region, which has been poorly surveyed by oceanic cruises.
These results were qualitatively confirmed by two other independent studies. -We first applied a cross-validation procedure (see Sect. 4. 1), which is a powerful technique for validating models  (Kohavi, 1995; Varma and Simon, 2006). We learned 30 different 2S-SOMs using 30 different learning dataset determined at random from the DPIG dataset (each learning dataset representing 90 % of DPIG) and 30 test datasets (10 % of DPIG). By averaging the results, we found that the 2S-SOM method retrieves the fucoxanthin concentration with a good score (see the statistical parameters in Table  2 ), which confirms the pertinence of the method. Ocean Sci. , 16, 513-533, 2020 www. ocean-sci. net/16/513/2020/ Figure  16. For ship stations 1, 2, 3, 5a and 5b, we show the averaged spectrum of the in situ spectra of the UPSEN stations in blue and the spectrum of the referent vector (in red) of the 2S-SOM neuron that has captured the closest satellite observations to the UPSEN station.
Among the different spectra constituting the referent spectrum, the spectrum of the learning database (DPIG) that is the closest to the averaged satellite spectra is shown in black. In the rectangular boxes, we show the position of the UPSEN station, the number of the neuron of the 2S-SOM that has captured the satellite observation, the Rfuco of the referent vector, the Rfuco DPIG of the closest DPIG and the in situ Rfuco UPSEN. -We then found that our fucoxanthin climatology is in agreement with in situ observations of phytoplankton reported in  Blasco et al. (1980)  in March to May 1974 off the coast of Senegal during the JOINT I experiment. These authors analyzed 740 water samples collected with Niskin bottles at 136 stations extending along a line at 21 • 40 N (in the northern part of the studied region) from 0 to 100 km offshore.
It also allows us to better understand the dynamics of the phytoplankton communities. The 2S-SOM method is efficient and rapid as soon as the calibration is done, since it uses elementary algebraic operations only. The 2S-SOM method is like a piecewise regression that takes advantage of the unsupervised classification of the SOM. We decomposed the DPIG database into quite a large number of partitions (9 × 8 = 162) when comparing our study to other studies  (Uitz et al. , 2006). The validity of the method has been controlled through a cross-validation procedure and confirmed by three qualitative studies. Statistical parameters (R 2 coefficients, RMSE and P values) of the cross-validation between the DPIG in situ pigments and the pigments given by the 2S-SOM averaged for the 30 2S-SOM realizations presented in Table  2  show the good performance of the method.
Acknowledgements. The study was supported by the  CNES  ( Centre National d'Etudes Spatales)  (project nos. CNES-TOSCA 2013-2014  and  2014-2015 ). The water-leaving reflectances were obtained from the  SeaWiFS daily reflectances , ρobsTOAw(λ), provided by  NASA/GSFC /DAAC observed at the top of the atmosphere (TOA) and processed with the SOM-NV algorithm (Diouf et al. , 2013) from 1998 to 2010. They are available at the following website: http://poacc. locean-ipsl. upmc. fr/(last access: 4  March 2020 ). The DPIG database was kindly provided by  Séverine Alvain. We thank  Alban Lazar  and  Eric Machu  for providing in situ data measured during the UPSEN experiments as well as stimulating discussions for their interpretation. We also thank  Ray Griffiths  for editing an earlier version of the paper.
Code and data availability. The satellite data (ocean color and SST) are available at the following website: http://poacc. locean-ipsl. upmc. fr/ (last access: 4 March 2020,  Diouf et al. , 2013). The DPIG database was kindly provided by Séverine Alvain (severine. alvain@univ-littoral. fr). The UPSEN data are available at alban. lazar@loceanipsl. upmc. fr. The 2S-SOM code is available on request at carlos. mejia@locean-ipsl. upmc. fr.
DNA was stained by Hoechst 33342 (Molecular Probes, Eugene, OR, USA) at a final concentration of 1 μg/ml. Black-and-white images were taken with a Zeiss AxioCam MRm CCD camera (controlled by AxioVision 40 software v4. 8. 2. 0) mounted on a Zeiss Axio Imager. M2 (Carl Zeiss AG, Oberkochen, Germany) epifluorescence microscope equipped with the appropriate filter sets to detect red, green and blue fluorescence. Individual images were acquired for each channel to detect Hop1-13myc, Rec7-GFP, Rad11-GFP and Hoechst 33342. Images were pseudo-coloured and overlayed using Adobe Photoshop CC (Adobe Systems Inc. , San José, CA, USA). Immunodetected Rec7-GFP and Rad11-GFP foci were counted on images of meiotic prophase I nuclei at the thread and network stages identified by the presence of immunostained Hop1-13myc linear elements  (Lorenz et al.
Data presentation and statistics Raw data is available on figshare (https://doi. org/10. 6084/m9. figshare. 11192861). Line graphs and bar charts were produced using Microsoft Excel 2016  (version 16. 0. 4638. 1000, 32-bit) , and scatter plots were generated in GraphPad Prism 5 for Windows (version 5. 04). Box-and-whisker plots were created in R (version i386, 3. 0. 1) (http://www. r-project. org/) (Lorenz et al. 2014). R was also used to compute Kruskal-Wallis test and Tukey's Honest Significant Differences employing the kruskal. test() and TukeyHSD() functions, respectively. Mann-Whitney U tests were performed as previously described  (Lorenz et al. 2014).
Acknowledgements We are grateful to  Miguel G. Ferreira ,  Kohli ,  Josef Loidl , Osman,  Gerald R. Smith ,  Walter W. Steiner  and  Matthew Whitby  for strains. We thank  Josef Loidl  for critically reading an earlier version of this manuscript. This work was supported by the  Biotechnology and Biological Sciences Research Council UK (BBSRC)  [grant number  BB/ F016964/1 ] and the  University of Aberdeen (College of Life Sciences and Medicine Start-up  grant to AL).
The DynACof model was designed to model coffee agroforestry systems and study the trade-offs to e. g. optimize the system facing climate changes. The model simulates net primary productivity (NPP), growth, yield, mortality, energy and water balance of coffee agroforestry systems according to shade tree species and management. Several plot-scale ecosystem services are simulated by the model, such as production, canopy cooling effect, or potential C sequestration. DynACof uses metamodels derived from a detailed 3D processbased model (MAESPA) to account for complex spatial effects, while running fast. It also includes a coffee flower bud and fruit cohort module to better distribute fruit carbon demand over the year, a key feature to obtain a realistic competition between sinks. The model was parameterized and evaluated using a highly comprehensive database on a coffee agroforestry experimental site in Costa Rica.
Software and data availability The DynACof model was developed as an R package  (R Core Team, 2019) , and as a Julia package  (Bezanson et al. , 2017). Full documentation is available on its dedicated website (https://vezy. github. io/DynACof), the code is open-source (GNU GPLv3 license) and available on Github repositories (R: https://github. com/VEZY/DynACof, Julia: https://github. com/VEZY/DynACof. jl) and archived on Zenodo (https://doi. org/10. 5281/zenodo. 1256816). The input data used for this study is available as example data from this repository, and the data for model evaluation is available from the FLUXNET website (http://www. europefluxdata. eu/home/site-details?id=CR-AqC).
, 2017)  and bienniality at the plant scale  (Schnabel et al. , 2018). Blossoming is mostly synchronized in sub-tropical regions but can also be highly asynchronous in equatorial regions, which impacts the distribution of fruit carbon demand and in turn, carbon allocation to other organs  (Rodríguez et al. , 2011). Third, coffee plants are often pruned every five to six years to sustain high levels of production on young resprouts. It is also assumed that the reserve compartment plays a major role in bean production, with biennial sprout dynamics  (Cannell, 1985). Fourth, there are very few comprehensive datasets that can be used to calibrate and test multi-objective models for ecosystem services, energy, carbon, and water balance, aboveground and belowground biomass, NPP, fruit yield and more.
 Another model was developed by  Two other models have also been applied to coffee in an agroforestry system using 3D light interception modules: in  Dauzat et al. (2001) , where only a sample of a few coffee plants were simulated, and using the MAESPA model to simulate the whole system. Since MAESPA was recently demonstrated to accurately predict light distribution, canopy temperature and water and energy balance in such systems  (Charbonnier et al. , 2013; Charbonnier et al. , 2017; Vezy et al. , 2018) , the model can readily compute all variables that are potentially influenced by the complex canopy structure. However, its relatively high computation time still limits its application for full rotations of coffee under AFS, and the model does not simulate growth and yield. We argue that a proper combination of the inherent strengths of the above-described models could provide significant improvements and extend application domains.