dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2006; Soppa et al. , 2014; Brewin et al. , 2015). (Dandonneau et al. , 2004; Guillard et al. , 1985)  0. 86 2. 04 0. 93 Coefficients based on global HPLC dataset corresponding the sum of the weighted diagnostic pigments to the total Chla; Chla= aDP Tara Ocean's HPLC measurements  (Pesant et al. , 2015)  were excluded from this global dataset. Since these HPLC measurements were performed on the same stations as for psbO, they are used in this study to evaluate the correspondence between pigments and phytoplankton groups.
Methods To extract the most information of the above-mentioned datasets, several machine learning algorithms were used in this study. Developing an operational algorithm that estimates from satellite information the phytoplankton groups' abundance was done using an unsupervised neural network called Self-Organizing map (SOM). The use of SOM and topology-constrained organization allowed us to uphold the non-linear relationships between phytoplankton group composition and satellite data through topology conservation. We tested different sets of learning hyperparameters and several combinations of satellite predictors to identify the optimal configuration of our algorithm. Once the training and the validation procedure have been done, the algorithm (called SOM-psbO) is operational and could be applied to satellite images to predict the phytoplankton groups' abundance on a global scale and daily from 1997 till the present.
Following that, to identify global large scale patterns upheld by SOM-psbO, a second algorithm was used, the Ascending Hierarchical clustering algorithm. This latter permitted us to emphasize the predominant data structure learned by the SOM-psbO and characterize phytoplankton biomes. To explain the potential divergence between DPA approach and psbO measurements, the last analysis serves to highlight the cumulative importance of a pigment composition to estimate a phytoplankton group abundance through a Random Forest approach. In the following section, each methodology and algorithm are explained in detail. 3. 1 Self-Organizing map applied to Tara Oceans psbO data (SOM-psbO).
Training of the SOM-psbO The SOM algorithm  (Kohonen, 2013)  aims to cluster a multidimensional database (D) into classes represented by a fixed network of neurons (the SOM map). This network is used to define a discrete distance between the neurons of the map, which present the shortest path between two neurons. Moreover, SOM enables the partition of D in which each cluster is associated with a neuron of the map and is represented by a prototype that is a synthetic multidimensional vector. Each observation of D will be assigned to the closest neuron, in the sense of the Euclidean Norm (EN). A fundamental property of a SOM is the topological ordering provided at the end of the clustering phase: two close neurons on the map represent data that are close in the data space. Indeed, the neurons are gathered so that two close observations of D (in the sense of EN) are assigned to two relatively close neurons on the map.
Using a number of neurons larger than the training data set allows discretization to be refined. In this case, some of them will capture a sample of the database, which permits to define a referent vector w for these neurons. When the neuron did not capture any data observation, the discrete distance between the neighboring neurons is used to determine the referent vector w of each neuron that has not captured any data  (Sarzeaud and Stephan, 2000; El Hourany et al. , 2019a). This leads to preserving the topological order provided by new interpolated neurons. Following that, the best map size was evaluated while calculating two sets of metrics for each increasing map size: -A quantification error and a topographic error: the quantification error represents the difference between an observation D and its closest neuron.
This error is monitored during the training procedure till it reaches stability at a minimum value with increasing training epochs. This is where the training should stop to prevent overfitting. However, the topographic error is a representation of having, for each observation of the database, distant best-matching neurons. This quantity is important to monitor in order to ensure the preservation of the topological order within the SOM map with an increasing number of interpolated neurons. -Mean regression coefficient and RMSE: in this case, for each given map size, D is used to cross-validate the SOM map. This is done using a one-leave-out procedure, where each observation of D is used iteratively either as a test or for training. Therefore, at each iteration of the cross-validation procedure, we calculate the closest neuron to the test observation on basis of its satellite variables only and associate these latter with the neuron's seven phytoplankton groups vector.
In order to regain information on each group's relative abundance, each Chla fraction of a neuron is divided by the total Chla value of this same neuron. Since the training was undergone using the whole phytoplankton structure at once, alongside the total Chla information, the SOM-psbO allows the inherent structure of the data to be preserved. For this phase, level 3 mapped 4 km daily images were used to estimate the phytoplankton group concentration at the same spatio-temporal resolution. However, since our dataset D is limited to a short number of data, naturally occurring cases might be missed, and not considered while using such a dataset. A robust quality evaluation of the output of this method should be quantified to prevent abnormal predictions to cases that are not seen within dataset D.
A reliability index was calculated between 1997 and 2021 by testing the set of values obtained for a given pixel against the values in the original dataset; if the value of a variable falls out of the bond defined by the +-2 Std around the mean value of the distribution, the variable is marked as distant. This is performed on all ten satellite variables per pixel. The reliability index is calculated by dividing the number of distant variables by the total number of existing variables to give an insight into how distant a pixel can be. With this definition, low values of the reliability index indicate that the method is reliable, and more care should be given to regions where the reliability index is larger.
Ascending Hierarchical Clustering applied on SOM-psbO A hierarchical ascending clustering (HAC) was used on SOM-psbO's neurons; The reason behind this further clustering on the neurons is to emphasize major non-linear relationships observed in the database; in this case, the HAC is used to describe potential phytoplankton community biomes across the global ocean. The HAC is a bottom-up algorithm for dataset clustering. The HAC starts from individuals and combines them according to their similarity (with respect to the chosen distance) to obtain new clusters. The exact number of biomes is not known a priori but at the end of the SOM+HAC procedure which suggests several possibilities of a number of clusters to be taken into account. A compromise is made between the number of clusters we can explain from a physical point of view and the number of clusters we need to include the maximum of information embedded in the dataset.
Evaluation of the importance of pigments to estimate phytoplankton groups using random forest Each psbO-derived phytoplankton group's abundance was associated with its corresponding HPLC pigments measurement performed on the same Tara Ocean's station. The importance of pigments to predict phytoplankton groups was computed using a Bagged Random Forest algorithm (number of learners set to 200), following the permutation-based importance method. The bagged random forest algorithm is a set of Decision Trees, each constituted of internal nodes and leaves. In the internal node, the selected feature (i. e. pigment in this case) is used to make a decision on how to divide the dataset into separate sets with similar responses in terms of a given phytoplankton group. Since this algorithm is used in a case of regression, the decision is evaluated while monitoring the error decrease between the real phytoplankton group abundance and the predicted one, which corresponds to the value of a divided set.
Results and discussion 4. 1 Cross-validation, performances, and spatial limitation of the SOM-psbO algorithm Ten combinations of satellite parameters were explored to estimate phytoplankton groups from Tara Oceans data (Fig. 3 ). The best combination of satellite parameters was: Chla, SST, PAR, Rrs at 4 wavelengths, bbp and Kd490. While using this set of satellite predictors, several SOM sizes were tested and the best set of maps with n ranging between 210 and 330 neurons was chosen based on the concordance of the maximum regression coefficient between estimated and observed phytoplankton values as well as the minimum error values of Quantization and topographic error, and the global RMSE related to all phytoplankton groups (Fig. 4 ). The cross-validation of the best combination shows a performance of an average R 2 of 0.
71 distributed between a maximal R 2 = 0. 86 for the green algae, and a minimal R 2 = 0. 56 for the dinoflagellates and cryptophytes (Fig. 5 , table  3 ). Upon summing all Chla fractions, the cross-validation analysis shows a satisfying agreement between estimated total Chla and in-situ values (R 2 = 0. 87) and therefore a preservation of the initial phytoplankton quantity expressed in total Chla. To evaluate the quality of the representation of the inter-variable relationships within the input data by the SOM, the correlation coefficients and phytoplankton groups values distributions were compared between the referent vectors (constituting the neurons) of the SOM and D. This analysis showed that the correlation coefficients were not altered within the SOM, as well as the shape of the distribution of the values while compared to the initial dataset.
These results highlight the capacity of SOM to preserve the characteristics of D after the training procedure (Fig. 6 ). However, regarding the limited size of the initial dataset, one should be cautious when applying SOM-psbO to the global satellite data. Through the quality control described in section 3 and performed on each pixel of the daily satellite image between 1997 to 2021, a global map was generated to illustrate the extent of the applicability of this method (Fig. 7 ). Regions 285 of low confidence can be identified where more than 40% of the pixels were masked throughout the time series between 1997 and 2021. These regions are mainly shown in coastal and turbid waters, and in the south pacific gyre, and are characterized either by very high or very low Chla values. This result is expected since the SOM algorithm is not able to extrapolate beyond the values' distribution of the initial dataset.
Furthermore, moderate confidence regions can be defined in which around 25% of the pixels fall out of the accepted bonds. And these regions are mainly concentrated in high latitudes, especially in the Southern Ocean, mainly due to the limited number of data points that sampled the area and the particular optical characteristics of that region  (Mitchell et al. , 1991). Table  3. Results of the cross-validation of SOM-psbO, and the HPLC-based validation based on different metrics; regression coefficient (R 2 ), root-mean-squared-error (RMSE), mean absolute error (MAE) and the Spearman correlation coefficient (Rsp).
Independent validation using global HPLC pigment dataset The global in-situ HPLC dataset was used to estimate Chla fractions for each phytoplankton group. This dataset was compared to its matching phytoplankton group's Chla fraction estimated using SOM-psbO and satellite data (Fig. 8 ). Evaluating the sum 295 of Chla fractions and comparing it with in-situ Chla can be considered as a baseline evaluation of this method. This comparison shows a satisfying correspondence scoring an R 2 =0. 7 with an RMSE of 0. 17 mg m -3. A relatively good correspondence is noted for the diatoms, showing an R 2 =0. 78 between in-situ and SOM-psbO's diatoms Chla fraction. Moderate correspondence is noted for dinoflagellates, green algae, haptophytes, cryptophytes, and pelagophytes, with an R 2 ranging between 0.
For prokaryotes, the outputs of  Xi et al. (2020)  show higher estimation, accentuated near the arctic and the equatorial region. And last, as for the dinoflagellates, the SOM-Pigments method shows lower values of Chla Dino, especially in subtropical gyres. Addressing the differences between the outputs referring to the same phytoplankton group is not a straightforward task. Two methods are based on DPA approach, the latter is not trivial due to uncertainties related to the choice of pigments to 350 delimit certain groups. Indeed, several studies showed that the DPA approach tends to overestimate some groups such as diatoms  (Brewin et al. , 2014; Chase et al. , 2020). This approach may compromise the relevance of satellite images when used. However, the added value of such an approach resides in the availability of the large HPLC dataset allowing the development of robust algorithms.
On the other hand, the method described in this paper and the generated outputs are based on a complete and harmonized database on the phytoplankton taxonomic community structure on a global scale; an approach that provides an unbiased picture of phytoplankton cell abundances. But the major limitation of this approach is the low number of observations which makes the global generalization of such a method a major challenge. The random forest analysis, performed using the in-situ Tara Oceans' psbO and HPLC measurements, highlights the need for a multivariate approach to predict the phytoplankton community structure from pigments (Fig. 12 ). Therefore, the variability of each group is best explained not only by one diagnostic pigment but the pigment composition as a whole. It is important to con-360 sider how natural variability may impact the interpretation of the pigment composition in terms of phytoplankton community structure.
Conclusions We found a remarkable congruence between the data derived from satellites and omics, despite the relatively small number 365 of omics data. The link has been made possible due to machine learning techniques and the preservation of the data structure using Self-Organizing maps. The methodology showed satisfying performances to provide robust estimates of seven major phytoplankton groups, with few limitations regarding the global generalization of the method. The size of the training database is essential to provide a straightforward easy generalizable method. However, in this case, estimates in regions like the subtropical gyres should be interpreted with caution. As DNA sequencing costs continue to decrease and new expeditions generate 370 molecular data from undersampled ocean regions, we expect the training datasets to increase in future years, and thus the accuracy of our method.
Furthermore, this study presented a valuable global dataset of relative quantities of phytoplankton groups based on a new molecular method, leading to unique inter-comparison with the DPA-based approach used on in-situ and satellite data to identify phytoplankton groups. Nevertheless, these datasets show different yet coherent information on the phytoplankton, valuable for the understanding of the community structure. The methodology presented in this work provides a unique opportunity to observe in real-time and high-resolution the state of the major phytoplankton groups at the global scale. This makes remote sensing observations excellent tools to collect EBVs, play the role of broker between monitoring initiatives and decision-makers, and provide a foundation for developing marine biodiversity forecasts under different policy and management scenarios.
Acknowledgements. The authors acknowledge the recommendations and guidance of  Emmanuel Boss  ( Pr. At University of Maine ) and  Sylvie Thiria  (Emeritus Pr. Sorbonne University ). R. E. acknowledges  CNES  postdoc fellowship 2019-2021,  CNES   TOSCA 2020-2021 , and  Sorbonne University Emergence program   2021-2023  ML4BioChange. J. J. P. K. acknowledges postdoctoral funding from the  Fonds Français pour l'Environnement Mondial. C. B. acknowledges  ERC Advanced Award Diatomic  (grant agreement No. 835067 ), and  French Government  ' Investissements d'Avenir' programs   OCEANOMICS  ( ANR-11-BTBR-0008 ),  FRANCE GENOMIQUE  ( ANR-10-INBS-09-08 ),  MEMO LIFE  ( ANR-10-LABX-54 ), and  PSL Research University  ( ANR-11-IDEX-0001-02 ). This article is contribution number xxx of Tara Oceans.
Code and data availability. psbO dataset: https://www. ebi. ac. uk/biostudies/studies/S-BSST761; Globcolour dataset: https://www. globcolour. info/, https://hermes. acri. fr/. SSTCCIdataset: https://data. marine. copernicus. eu/product/SST_ GLO_SST_L4_REP_OBSERVATIONS_010_024/description. Global HPLC pigment dataset: MAREDAT, POLERSTERN data, Labrador.
2. 3 3. 1 Satellite-derived phytoplankton Chla fraction (CMEMS/Globcolour dataset)This multi-sensor product contains the concentration of each phytoplankton functional type (expressed in terms of Chla concentration fraction) based on the Xi et al. (2020)  algorithm, processed from 1997 till present. This algorithm allows an estimate of the Chla concentration of diatoms, dinoflagellates, haptophytes, green algae, and prokaryotes. The algorithm was implemented using HPLC-based phytoplankton groups (based on DPA approach, Soppa et al. (2014)) and satellite reflectance in the visible spectrum (bands comprise between 400 and 681 nm) with empirical orthogonal function (EOF). This dataset is found on the CMEMS portal (product number: OCEANCOLOUR_GLO_BGC_L3_MY_009_103). 2. 3. 2 Satellite-derived phytoplankton pigments SOM-Pigments (El Hourany et al.
, 2019a) is a machine learning-based algorithm that allows the estimation of phytoplankton pigment concentrations in oceanic waters from satellite ocean color data (Chla, Rrs at four wavelengths: 412, 443, 490 and 555nm) and SST. this algorithm is based on the use of Self-Organizing maps (SOM) and was calibrated using a global HPLC dataset which includes 10 phytoplankton pigment concentrations: Chlorophyll-a (Chla), Divynil-Chlorophyll-a (DVChla), Chlorophyll-b (Chlb), Divynyl-Chlorophyll-b (DVChlb), 19'Hexfucoxanthin (19HF), 19'Butfucoxanthin (19BF), Fucoxanthin (Fuco), Peridinin (Perid), Alloxanthin (Allo), Zeaxanthin (Zea). The results of the cross-validation procedure scored a regres-https://doi. org/10. 5194/egusphere-2022-1421 Preprint. Discussion started: 14 December 2022 c Author(s) 2022.
This was accompanied by a rapid densification of the cGPS network and reoccupation of eGPS benchmarks  (Joseph et al. 2022). At La Soufrière, human resource limitations prevented the real-time identification of precursory inflation from the then 2-station cGPS network. Infrequent eGPS occupations, a restricted pre-2021 cGPS network geometry, combined with a lack of volcanic activity since the start of GPS monitoring, have impeded attempts at characterizing the volcano's plumbing system architecture before now. Here, we describe the expansion of geodetic monitoring following the onset of the 2020-21 eruption of La Soufrière volcano. This includes the deployment of additional GPS stations and the incorporation of InSAR datasets. We then retrospectively analyse available cGPS and InSAR (ALOS-2, SAOCOM-1 and Sentinel-1) time-series from 2018 for possible precursory deformation, and assess their individual effectiveness for geodetic monitoring in this region.
GPS From inception, cGPS monitoring in St Vincent has been done using dual-frequency receivers (Table  1 ). Data are collected using a 30 s sampling interval for a 24 h per UTC day. Instruments also record data at a higher sampling rate (1 s intervals); however, these are not routinely stored. Earliest installations SVGB (Belmont Observatory) and SVGK (Kingstown), located c. 9 and c. 19 km away from La Soufrière respectively (Fig. 1 ), have their antennas mounted on the roofs of buildings, using stainlesssteel pins secured in cement. The first indicator of precursory unrest related to this eruption was elevated seismicity in November 2020  (Joseph et al. 2022). Due to scientific contractual changes, this timing also coincided with the resumption of daily cGPS data processing alongside the backfilling of processing from earlier months.
InSAR Despite the extensive spatial and temporal coverage expected from monitoring deformation fields using InSAR, this technique has not yet been incorporated into routine volcano monitoring by UWI-SRC. Contributing factors have ranged from budgetary to feasibility. Historically, there has been a lack of in-house specialist experience needed for interpreting satellite datasets. The application of InSAR to tropical volcanoes is also known to be inhibited by dense vegetation, large variations of water vapour and steep topography  (Ebmeier et al. 2013) , features which prevail in the Eastern Caribbean. Coherence is improved with increasing radar wavelengths, but the corollary of compounded limitations to freely available images is also true. Long satellite repeat-times (InSAR requires at least a pair of SAR images and preferably a longer time-series) are an added hindrance towards real time InSAR measurements during unrest or eruptive episodes  (Ebmeier et al.
GPS In preparation for processing, binary format receiver data collected from GPS sites are converted into standard Receiver Independent Exchange (RINEX) format using the program TEQC  (Estey and Meertens 1999). Raw receiver data and converted RINEX observation and navigation files are archived by UWI-SRC. Daily positions are calculated with GAMIT/GLOBK software (version 10. 71)  (Herring et al. 2018 ) using over 100 global (International Global Navigation Satellite Systems Service, IGS) reference stations with final orbits. To minimize tectonic influences, time-series of station positions are calculated relative to the ITRF2014 Caribbean Plate motion model as defined by  Altamini et al. (2017).
e. taking place at depth. 2-3 km under the crater). In order to provide constraints on deeper processes, we include images acquired by L-band satellites ALOS-2 and SAOCOM. Contrary to C-band, SAR data acquired in L-band have the capability of penetrating through vegetation, which gives access to deformation at the edifice scale, gaining resolution on processes taking place at greater depth. We processed ALOS-2 images between October 2014 and April 2021 (Table  S1a ) using GAMMA version 2. 1  (Werner et al. 2000)  and performed topographic corrections using the 30 m ASTER GDEM. Because of limited coherence in many of the interferograms due to long temporal baselines and dense vegetation across large parts of St Vincent, we used StaMPS  (Hooper et al. 2007 ) to identify and extract stable scatterers to be used in forming an PS-SBAS time-series.
(2022). Using ALOS-2 and SAOCOM data, negative LOS displacements of c. 80 mm were measured on the western edge of the crater consistent with uplift and/or westward motion (Fig. 6 ). Due to the timing of these acquisitions, both datasets contain the   2. Yellow and red bars correspond to effusive and explosive eruptive phases respectively. Included on the bottom panels are daily volcanic earthquake counts for the same period, as recorded by UWI-SRC. Volcanic earthquakes above 50 per day are not shown. displacement associated with the onset of the effusive eruption detected by Sentinel-1. The longer wavelength of ALOS-2 and SAOCOM radar facilitated higher coherence away from the dome exposing a slightly broader deformation signal than what was resolved using Sentinel-1 data. This points towards an additional shallow deformation source occurring sometime between October 2020 and January 2021 based on image acquisition dates.