dataesr/scientific-paragraphs-categorization huggingface dataset:
1 mM RO201724, a phosphodiesterase inhibitor (Sigma-Aldrich). The accumulated cAMP was quantified using the cAMP Dynamic 2 Kit (PerkinElmer Cisbio) according to the manufacturer's protocol. Fluorescent signals were measured at 620 and 665 nm on a Spark 20M multimode microplate reader (Tecan). Data were plotted as the FRET ratio [10,000 × (665/620)] as a function of AVP concentration [log (AVP)]. Data were analyzed with GraphPad Prism v9. 1. 1 (GraphPad Prism Software Inc. ) using the "dose-response stimulation" subroutine. Median effective concentrations were determined using the log(agonist) versus response variable slope (four-parameter) fit procedure. Experiments were repeated at least three times on different cultures, with each condition in triplicate. Data are presented as means ± SEM (fig.
Understanding the relationship between surface marine ecosystems and the export of carbon to depth by sinking organic particles is key to represent the effect of ecosystem dynamics and diversity, and their evolution under multiple stressors, on the carbon cycle and climate in models. Recent observational technologies have greatly increased the amount of data available, both for the abundance of diverse plankton groups and for the concentration and properties of particulate organic carbon in the ocean interior. Here we use synthetic model data to test the potential of using Machine Learning (ML) to reproduce concentrations of particulate organic carbon within the ocean interior based on surface ecosystem and environmental data. We test two machine learning methods that differ in their approaches to data-fitting, the Random Forest and XGBoost methods.
The synthetic data is sampled from the PlankTOM12 global biogeochemical model using the time and coordinates of existing observations. We test 27 different combinations of possible drivers to reconstruct small (POCS) and large (POCL) particulate organic carbon concentrations. We show that ML can successfully be used to reproduce modelled particulate organic carbon over most of the ocean based on ecosystem and modelled environmental drivers. XGBoost showed better results compared to Random Forest thanks to its gradient boosting trees architecture. The inclusion of Plankton Functional Types (PFTs) in driver sets improved the accuracy of the model reconstruction by 58% on average for POCS, and by 22% for POCL. Results were less robust over the Equatorial Pacific and some parts of the high latitudes.
Data and Methods. In this section we describe a set of variables that will be used to test the ML method's ability to reconstruct particulate organic carbon concentrations based on ocean model data. We create a set of synthetic data by sampling a model at the time and location of real-world observations. We discuss the availability and distribution of real-world observations and their limitations. In this section we also describe the PlankTOM12 global ocean biogeochemical model and how we use it to develop a ML method and test its ability to reconstruct small and large particulate organic carbon with a limited number of observations. To provide resemblance to the real data availability we focus on the period 2009-2013 which guarantees additional sampling of co-located biological, chemical, and environmental variables from the Tara expeditions  (Sunagawa et al.
, 2020). Two sets of data are needed to test the Machine Learning method: a set of targets and a set of drivers. The drivers represent the input variables to the ML method (here the biological, chemical, and environmental variables). The targets represent the variables we are trying to reconstruct (here the particulate organic matter POCS and POCL). The ML will then determine the relationship between the drivers and targets, which can then be applied in regions where drivers are available to infer targets where the later data do not exist. We use observations of particle distribution in two ways. First to determine the time and location of the observations, and second to verify that the ocean model is of sufficient quality to be used in this analysis. The sampling of the particulate organic carbon concentration is based on the data from an Underwater Vision Profiler 5 (UVP5)  (Gorsky et al.
The NEMO-PlankTOM12 Global biogeochemical model. We used the output from the NEMO-PlankTOM12 coupled physical-biogeochemical model of the global ocean at daily and monthly time resolution. NEMO represents physical transport processes and is used in its v3. 6-ORCA2 version, with a horizontal resolution 2º longitude and 0. 3º to 1. 5º latitude, and 31 vertical levels. It is forced by daily meteorological data from NCEP reanalysis  (Kalnay et al. , 1996)  over the period 1948-2020, with output for 2009-2013 used here. This model version is identical to that used to estimate the ocean CO2 sink in the Global Carbon Budget 2021 annual update  (Friedlingstein et al. 2021). PlankTOM12 represents sinking processes through the explicit representation of two organic particle of different size, with small particles sinking at a constant speed of 3 m/d, and larger particles sinking at a variable speed between 3 and 150 m/d depending on the ballast effect of their mineral content  (Buitenhuis et al.
, 2013). In addition, a dissolved organic carbon component is transported via ocean currents. Particles are generated through mass flux from the PFTs resulting from mortality and egestion and from aggregation through differential sinking or turbulent coagulation, and destroyed through grazing by zooplankton and remineralisation by bacteria and through disaggregation from shear currents. Large PFTs contribute mostly to POCL, while small PFTs contribute mostly to POCS. (Le  Quéré et al. 2016;  Fig. 2 ). The NEMO-PlankTOM12 model output was sampled at the time and location identified from the observations mentioned above to create a synthetic dataset. The model grid-coordinate closest to the real geographical position was chosen. If several measurements were co-localised at the same grid coordinate and same time step (day for daily PlankTOM12 and month for monthly PlankTOM12 outputs), it is counted as one measurement.
Method. We tested 2 ML methods that are widely used in target's reconstruction based on tabular data sets: the Random Forest regressor and the XGBoost (Extreme Gradient Boosting) regressor. The Random Forest (RF) regressor is an ensemble algorithm that contains a number of decision trees on various subsets of the given dataset and takes as output the average of prediction from each tree estimator. RF can run several trees at the same time allowing a use of a large number of input variables, and it is robust to overfitting  (Biau, 2012). XGBoost (XGB) regressor is an effective treebased ensemble learning algorithm (Chen and Guestrin, 2016). It builds several models sequentially where each new model attempts to correct errors from the previous one. XGBoost uses the gradient descent algorithm to minimise the loss function of the model.
Using RF and XGBoost we can estimate the driver importance to identify which driver has the greatest impact on the predictions. To check the driver importance, we use drop_col_feat_imp python function In the present study the available data were split into training and validation data sets (Fig. 3a ). Validation data is not included in RF training, however we use it to test the performance of trained RF and tune hyperparameters afterwards. The test data are taken from the regions where there are no observations (Fig. 3b ): 3 months for each year from the period 2009-2013 and 6 positions for each month were chosen randomly. This will allow us to identify the possible accuracy of reconstruction that can be reached in these regions when we will apply a developed method to real observations. However, when POCS and POCL will be reconstructed using only real-world observations, we will need to split all available data into training, validation and test data sets.
In-situ measurements are obtained at a particular time of the day and a particular latitude-longitude position while the model provides estimations over the day (or month) and on the model grid (2º longitude and mean 1. 1º latitude resolution). We concluded that observed and modelled POCS and POCL have a common tendency in their vertical distributions. However, among other things, differences in amplitudes may affect our findings in this work when we develop a ML method based on observations only. Due to the constraint in data availability further we use monthly PlankTOM12. Before developing a ML method, we investigate the interactions between targets and drivers in the model. Table  2  shows the correlation coefficients between the POCS and POCL and corresponding drivers that can influence POCS and POCL variability.
XGBoost regressor builds the model sequentially improving it at each iterative step. At each iteration, XGBoost regressor analyses the prediction and gives more weight to the data where the fit is still wrong. It is a good tool for an unbalanced data set, like in our case where the data of particulate organic carbon concentration are sparse in time and space. We tested the influence of a wide range of environmental and ecosystem drivers on POCS and POCL reconstruction. The introduction of Plankton Functional Types (PFTs) in the driver set greatly improves the fit and shows a linkage between surface ecosystem structure and particulate organic carbon distribution within the ocean interior. We with a good cover -regions in the Atlantic Ocean. However, we found that the Southern Ocean is a more sensible region to the driver set's composition.
The observational data is particularly sparse in this region and our analysis suggests that identifying the drivers of importance based on real dataset will be difficult. Here we showed that the XGBoost regressor and Random Forest are suitable for this problem and can reconstruct modelled POCS and POCL with appropriate accuracy. This is evidenced from the globally averaged correlation coefficient up to 0. 88 for POCS and 0. 68 for POCL, and the globally averaged RMSE up to 20 % (0. 08 μmol/L) of standard deviation of PlankTOM12 POCS, and 65% (0. 028 μmol/L) of standard deviation of PlankTOM12 POCL. ML outputs represent well the spatial patterns of POCS and POCL distribution. However, the validity of the approach on observations is dependent on the availability of co-located information on the drivers of importance.
For some drivers this should be possible (e. g. environmental conditions and chlorophyll a), while for other drivers information is more sparse (e. g. the PFTs). Our analysis suggests that additional PFT observations would help provide broader insights into the distribution of POC in the ocean. The next step of this work is to apply ML to real data using methods from the present study. This study provides insights on the drivers that may be responsible for POCS and POCL variability and regional dependencies. However, the dependencies are simply returning the outcome of complex ecosystem processes among the drivers as represented in the PlankTOM12 model. Although these processes are based on current understanding and a broad range of observations (Le  Quéré et al. , 2016; Wright et al. , 2021; Buitenhuis et al.
https://gist. github. com/erykml/6854134220276b1a50862aa486a44192). This method estimates how the accuracy of the ML output changes if one of the drivers is dropped off from a driver set (DS) based on the training dataset. Effective ML algorithm requires sets of training, validation and test data. The training data builds up the ML model. Model evaluates training data repeatedly to learn about the relationship between inputs (driver set) and known outputs (target set) and adjusts itself to better represent the target. The purpose of validation data is to evaluate the model during its training by introducing new unseen data. It allows us to evaluate how a developed model works on a new dataset and to optimise hyperparameters. The test data evaluate the final accuracy of the ML model and confirm that the model works correctly on any unseen data.
Figure5shows the statistics of POCS reconstruction using RF and XGB. XGB (orange) generally overperforms RF (blue). The statistics are slightly worse for the validation and test data sets, as expected. For reconstructions using XGB, the RMSE and absolute bias are about 0. 05 μmol/L and 0. 03 μmol/L on the training data set and vary around 0. 1 μmol/L and 0. 05 μmol/L, on the validation and test data, respectively. Correlation coefficients (Fig. 5g, h, i) have high values on all datasets showing that the vertical profiles of POCS have a correct shape. These results show that the available spatial and temporal coverage of in situ observations can be sufficient to reconstruct POCS with an appropriate accuracy over the global ocean. The analysis of global maps (shown below) will help to identify areas with low accuracy and their differences with training regions.
Acknowledgements. The authors would like to thank  Jean-Olivier Irisson  for his contribution in the development of the methodology. ADS, ETB and CLQ acknowledge support from  Royal Society  (grant  RP\R1\191063 ), and  NERC Marine Frontiers  project (grant  NE/V011103/1 ) for CLQ. RK acknowledges support via a  "Make Our Planet Great Again" grant  from the  French National Research Agency  within the " Programme d'Investissements d'Avenir " (grant no. ANR-19-MPGA-0012 ) and by the  Heisenberg program  of the  German Science Foundation  under project number  469175784.
Data and code availability. PlankTOM12 data used within this study are available at https://doi. org/10. 5281/zenodo. 7324781. UVP5 data can be found at https://doi. org/10. 1594/PANGAEA. 924375 (R. Kiko et al. , 2021). Codes for data preparation, development of machine learning methods and tests of different driver sets as well as codes that provide figures shown in the article can be found at https://doi. org/10. 5281/zenodo. 7326992. Author contribution. All authors contributed to the development of the methodology. ADS, CLQ, ETB designed the experiments, and ADS carried them out. ADS developed codes and performed the simulations. ADS prepared the paper with contributions from all coauthors.
( https://gist. github. com/erykml/6854134220276b1a50862aa486a44192). This method estimates how the accuracy of the ML output changes if one of the drivers is dropped off from a driver set (DS) based on the training dataset. Effective ML algorithm requires sets of training, validation and test data. The training data builds up the ML model. Model evaluates training data repeatedly to learn about the relationship between inputs (driver set) and known outputs (target set) and adjusts itself to better represent the target. The purpose of validation data is to evaluate the model during its training by introducing new unseen data. It allows us to evaluate how a developed model works on a new dataset and to optimise hyperparameters. The test data evaluate the final accuracy of the ML model and confirm that the model works correctly on any unseen data.
4 and 63 • N. The evolution of the ISOW properties from the Iceland Basin to the Irminger Sea is attributed to mixing through the complex bathymetry of the ridge. Analyzing microstructure and conductivity-temperature-depth (CTD) data from the OVIDE line,  Ferron et al. (2016)  showed that dissipation rates (vertical mixing) were enhanced over the flanks of the Reykjanes Ridge. Similarly, fracture zones are known sites Published by Copernicus Publications on behalf of the European Geosciences Union. T. Petit et al. :  Deep through-flow in the Bight Fracture Zone of large modification of water mass properties  (Mercier et al. , 1994). Recent studies based on moorings  (Bower and Furey, 2017)  and Deep-Argo floats  (Racapé et al. , 2019)  deployed in the Charlie-Gibbs Fracture Zone (CGFZ) have pointed out the mixing between ISOW and surrounding water masses, including Northeast Atlantic Deep Water, Lower Deep Water and LSW.
The maximum depths reached by the S-ADCP signal were 1300-1400 m for the OS38, 700-800 m for the OS75 and 200-250 m for the OS150. The calibrations and processing of these measurements were identical for all four cruises. As described by  Petit et al. (2018) , the OS150 datasets were used to correct the calibration of the second S-ADCP, and the velocity profiles were averaged over 2 km segments along the sections. These measurements were used to estimate absolutely referenced geostrophic velocities perpendicular to the sections and the associated gridded transports (Fig. 2  and Table  3 ). The geostrophic velocities were estimated from the CTDO 2 measurements using the seawater toolbox and were refer-enced by velocity measurements from the OS38 and OS75  (Petit et al. , 2018). The geostrophic velocities were then evaluated by comparison to the L-ADCP velocities at the East and Middle sections (Figs.
A3  and A4 ). Note that L-ADCP measurements provide local measurements of the total velocity field at each hydrographic station while geostrophic velocities are averaged velocities between two successive stations  (Lherminier et al. , 2007) , so we cannot expect a perfect agreement between the two datasets. Nevertheless, the comparison of the amplitude and spatial structure of the velocity field reveals the remarkable agreement between the geostrophic and L-ADCP velocities for each section. The largest difference in velocity is found at the East section in 2017 where the geostrophy-based velocity in the ISOW core is about -0. 36 m s -1 , while the L-ADCP-estimated velocity is about -0. 14 m s -1.
Deep-Arvor floats Data from three Deep-Arvor floats are used to assess the pathway and hydrographic evolution of ISOW as it flows westward through the BFZ. The Deep-Arvor floats are Argo Table  1. Summary of the sections occupied during the four hydrographic cruises and the associated station numbers. Details on the cruises and on the locations of the stations can be found in  Branellec and Thierry (2016) ,  Branellec and Lherminier (2017) ,  Branellec and Thierry (2018), and Branellec et al. (2019)  Float 6901603 grounded at cycles 2, 11 and 12 and drifted at shallower pressure during these cycles. Trajectories within the ISOW layer of the Deep-Arvor floats are considered from cycle 2 because the floats are programmed to rise to the surface immediately after reaching their parking depth at the end of the first descent.
Temperature, practical salinity and pressure were measured using a Seabird SBE41CP CTD sensor with a target accuracy of 0. 002 • C, 0. 004 and 7 dbar, respectively. The salinity data were corrected following the procedure specified for Deep-Argo floats  (Cabanes et al. , 2016; Wong et al. , 2021). A compressibility term (CPcor) was applied to correct a pressure-dependent conductivity sensor bias. For the three floats, the nominal CPcor value provided by Seabird (CPcor = -9. 57 × 10 -8 dbar -1 ) is replaced by the Argo recommended value, CPcor = -13. 5 × 10 -8 dbar -1. No further salinity bias or drift was detected for 6901603 and 6902882, while 6902881 was corrected for a salinity bias of 0. 002. This bias was determined by comparison with a ship-based calibrated cast acquired at float deployment.
A comparison of the velocity fields observed in 2015 and 2017 in the ISOW layer of the East section reveals differences in the amplitude and location of the ISOW throughflow (Fig. 2 ). In 2015, the westward flow is intensified at the center of the sill (56. 74 • N), while a weak eastward flow is observed along its northern wall (56. 77 • N). In 2017, the  westward flow is observed all along the section and is intensified along the northern wall of the sill. Our dataset does not allow us to determine whether these differences are due to temporal variability of the inflow from the Iceland Basin or to differences in the local bathymetric constraints within the narrow channel of the BFZ entrance, as the East section in 2015 is localized slightly upstream in the channel compared to the East section in 2017 (Fig.
Impact of the BFZ through-flow on the hydrography of NADW To assess the evolution of ISOW as the overflow exits the BFZ through the two western channels, we use L-ADCP profiles at the West section in 2015 and two Deep-Arvor floats deployed at the Middle section in 2018, 6902881 and 6902882. The analysis is based on L-ADCP instead of geostrophic velocities because the distance between the stations at the West section is too large to resolve the ISOW flow in these narrow channels. The data suggest that the main exit pathway for ISOW follows the northern walls of the western channels and that incursion of dense water from the Irminger Sea follows the southern walls, leading to a mixing between NADW and ISOW from the BFZ. Indeed, the RREX15 ship-based data show that each of the western channels is composed of a westward flow along the northern walls (stations 16 and 18) and an eastward flow along the southern walls (stations 17 and 19) in the ISOW layer (Fig.
Discussion and conclusion A combination of new and insightful datasets allows us to investigate the role of the BFZ as a new source of ISOW for the NADW spreading in the Irminger Sea. We first document ISOW pathways within the BFZ and its hydrographic evolution by analyzing data from hydrographic sections and three Deep-Arvor floats deployed at specific locations through the BFZ between 2015 and 2018. From east to west, the hydrographic sections were acquired at the eastern sill of the BFZ, within the rift valley and through western channels at the exit of the BFZ. This combination of observational datasets reveals that the BFZ is mainly fed by the lighter part of the ISOW layer flowing west of 29-30 • W near 58 • N, as part of the western and middle branches of the East Reykjanes Ridge Current in the Iceland Basin  (Daniault et al.
Acknowledgements. The authors thank all colleagues and ship crews involved in the RREX and OVIDE cruises during which the hydrography data were obtained. Financial support. This research has been supported by the  French government  within the framework of the " Investissements d'avenir" program  managed by the  Agence Nationale de la Recherche  under the  NAOS  project (grant no. ANR-10-EQPX-40 ) and  ARGO-2030  project (grant no. ANR-21-ESRE-0019 ). The  OVIDE  project was supported by  CNRS ,  Ifremer , the national program  LEFE (Les Enveloppes Fluides et l'Environnement ) and the  Spanish Ministry of Sciences and Innovation co -funded by the  BOCATS2  project (grant no. PID2019-104279GB-C21 ) and  ARIOS  project (grant no. CTM2016-76146-C3-1-R ). Review statement. This paper was edited by  Ilker Fer  and reviewed by three anonymous referees.
Data availability. The hydrographic data obtained during the four cruises are archived online at https://doi. org/10. 17882/55445 (Thierry et al. , 2018)  for RREX15 and RREX17, at https://digital. csic. es/handle/10261/154341, last access: 8 July 2022  (Pérez et al. , 2017)  for BOCATS16, and at https://doi. org/10. 17882/87394 (Lherminier et al. , 2022)  for OVIDE18. The Deep-Arvor data of the floats used in this study are available at the Argo float data and metadata from the Global Data Assembly Centre (Argo GDAC), https://doi. org/10. 17882/42182 (Argo, 2022). These data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo. ucsd. edu, ARGO, last access: 8 July 2022, https://www. ocean-ops. org, OceanOPS, last access: 8 July 2022).