dataesr/scientific-paragraphs-categorization huggingface dataset:
335 (xCO 2 ) data, and so the same method of matching temporal and spatial grid points was used to fill in these fields using the GLOBALVIEW CO 2 dataset from the US National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) (GLOBALVIEW-CO2, 2013). For ease, these additional wind speed and xCO 2 data were downloaded, extracted and then inserted into the tab delimited in situ file using some simple custom python scripts but the same process could be 340 performed manually. These scripts are not part of FluxEngine but the functionality they provide will likely be available as part of the planned interactive Jupyter tutorials, see Sect. 4. The in situ data were collected from different ships and underway systems, all sampling water at different and unknown depths.
These measurements are typically collected from a few metres below 345 the water surface, whereas the CO 2 concentration (combination of fCO 2 and solubility) either side of the mass boundary layer is required for an accurate gas flux calculation. Before these data from multiple sources can be used for an accurate gas flux calculation, they need to be reanalysed to a common temperature dataset and depth  (Goddijn-Murphy et al. , 2015; Woolf et al. , 2016). Therefore the reanalyse_socat_driver. py tool was first used to reanalyse all fCO 2 data to a consistent temperature 350 and depth. In the absence of coincident in situ skin (or sub-skin) temperature data, the slow re-equilibrium time of CO 2 in seawater (i. e. on the order of months for CO 2 to equilibrate with the atmosphere) ensure that monthly mean, or rolling monthly mean (centred on the day of interest) skin or sub-skin sea surface 355 temperature (SST) values are suitable for re-analysing the in situ data.
Arguably a robust daily skin or sub-skin SST value would be better, even if that is obtained by a seasonal curve fitted to the monthly values and interpolated to the day of interest. Here for simplicity monthly mean sea surface temperatures from the Reynolds Optimally Interpolated Sea Surface Temperature dataset (OISST,  Reynolds et al. , 2007)  were used as the reference subskin temperature dataset, resulting in reanalysed 360 fCO 2 that are valid for the bottom of the mass boundary layer (termed sub-skin within  Woolf et al. , 2016). The reanalysed fCO 2 were then inserted into the tab-delimited in situ dataset producing a single dataset. The tab-delimited file was then converted into a netCDF format file using the text2ncdf. py tool. This.
365 tool groups all data according to a user-specified spatial sampling grid, calculating the mean value and standard deviation for each cell within the grid as well as the number of data that were used to calculate these statistics. Here, for simplicity, the spatial resolution was defined as 1 o × 1 o grid. FluxEngine was The sea-to-air CO 2 fluxes were then calculated using the rapid model (see Eq. (  1 ) and Woolf et al. , 375 2016) and was run using a quadratic wind speed based gas transfer velocity parameterisation  (Ho et al. , 2007). To identify the impact of the fCO 2 reanalysis stage, the sea-to-air CO 2 flux calculation was repeated using the original in situ fCO 2. Figure  3a  shows the resultant calculated CO 2 flux along each of the cruises (1-4). The southern sub-380 tropical part of the cruise track 1 represents an area of the ocean that is a sink of CO 2 (negative sea-toair flux).
Interestingly, there are also examples (e. g. The new FluxEngine support for calculating gas fluxes from fixed stations uses the temporal dimension of the input files, creating output files of the same dimension that can be easily visualised as a time series. Data for the Östergarnsholm monitoring station covering a period from 28 th January 2015 to the 415 9 th September 2015 were downloaded from the data repository  (Rutgersson, 2017). These data contain in situ measurements for fCO 2 , salinity and temperature, model reanalysis air pressure at sea level from the National Center for Environmental Prediction, National Center for Atmospheric Research (NCEP/NCAR) dataset  (Kalnay et al. , 1996) , xCO 2 from the NOAA ESRL GLOBALVIEW dataset (GLOBALVIEW-CO2, 2013) and World Ocean Atlas salinity data  (Boyer et al.
, 2013). CCMP wind 420 speed data were extracted and added to the tab delimited in situ dataset using the same method as used in case study 1 (Sect. 3. 1). For gridded input data a single grid point containing the Östergarnsholm station location was selected from a global 1° × 1° projected grid. The text2ncdf. py tool was configured to convert the text formatted data file into a single netCDF file 425 using a temporal resolution of one day. This produced a netCDF file with a temporal dimension size of 246 (days), containing the daily mean value for each of the 246 days covered by the dataset. FluxEngine was configured to use this file as input, and to index into the temporal dimension appropriately. The fCO 2 data were reanalysed using the same method and data as used in case study 1 to determine fCO 2 at the bottom of the mass boundary layer.
430 The flux calculation used the rapid model  (Woolf et al. , 2016)  with the  Nightingale et al. (2000)  wind based gas transfer velocity parameterisation and was performed separately using the reanalysed fCO 2 and original fCO 2 data. The temporal resolution was set to provide daily calculations for each of the 246 days allowing seasonal variations to be observed, but not diurnal variations. FluxEngine supports 435 arbitrary temporal resolutions to within minute precision and the choice predominantly depends on the resolution of the available data and the particular research questions to be addressed. FluxEngine was configured to write output into a single netCDF file as a time series. Figure  5  shows the time series of SST, wind speed, xCO 2 , fCO 2 , concentration of CO 2 and calculated sea-to-air CO 2 flux.
Case study 3: Surfactant suppression of N 2 O gas fluxes using the MEMENTO database Nitrous oxide (N 2 O) and methane (CH 4 ) are both climatically important gases. In the troposphere, they act as greenhouse gases (IPCC, 2013), whereas stratospheric N 2 O is the major source for NO radicals 445 which are involved in one of the main ozone reaction cycles  (Ravishankara et al. , 2009). Source estimates indicate that the world's oceans play a major role in the global budget of atmospheric N 2 O and a minor role in the case of CH 4  (IPCC, 2013). Oligotrophic ocean areas are near equilibrium with the atmosphere and, consequently, make only a relatively small contribution to overall oceanic emissions, whereas biologically productive regions (e. g. , estuaries, shelf and coastal upwelling areas) 450 appear to be responsible for the major fraction of the N 2 O and CH 4 emissions  (Bakker et al.
460 to apply the methodology of  Pereira et al. , (2018)  to in situ data from the MEMENTO (MarinE MethanE and NiTrous Oxide) database  (Kock and Bange, 2015)  in order to estimate the equivalent suppression effect on the exchange of N 2 O between ocean and atmosphere. While FluxEngine is able to calculate sea-to-air fluxes of both N 2 O and CH 4 , we confined our analysis Infrared Sea Surface Temperature Autonomous Radiometer (ISAR)  (Donlon et al. , 2008)  and therefore future AMT datasets will include direct sea skin temperature measurements. Using skin temperature 475 measurements are likely to further increase the accuracy of the flux calculation. A custom gas transfer velocity parameterisation was implemented following the template provided in the toolbox to calculate the gas transfer suppression due to biological surfactants in surface waters.
2 into the final depth relationship. The process of fitting of the depth relationship and calculating ε at depth z=0. 2 was implemented using a custom pre-processing function that is included as an example in the FluxEngine download. This demonstrates how pre-processing functions can be used to perform complex data processing. et al. , 2007) , salinity data from the NOAA World Ocean Atlas  (Zweng et al. , 2018) , atmospheric molar fraction of CO 2 in dry air data from the GLOBALVIEW CO 2 dataset (GLOBALVIEW-CO2, 2013), and fCO 2 data from the SOCAT derived sea-to-air CO 2 flux reference dataset for 2010  (Woolf et al. , in-review). Since the  Zappa et al. , (2007)  relationship was parameterised in low to moderate wind speeds and in shallow marine environments, a mask was set in the 550 configuration file to constrain the calculation to grid cells with wind speeds less than 10 m s -1 and shelf sea water depths between than 20 m and 200, and 20 and 500 m.
These depth ranges were chosen to be consistent with previous studies (e. g. Laruelle et al. , 2018; Shutler et al. , 2016). The ofluxghg_flux_budgets. py tool was used to compute the annual integrated net sea-to-air flux in all The FluxEngine toolbox will continue to be developed in response to new advances in research. To increase user-uptake future work will include a series of iPython Jupyter notebooks. These online and interactive iPython notebooks will allow users to investigate the toolbox without the need to install any 575 software. Users will be able to modify and re-run the notebook and immediately see the impact of any changes. This approach has been previously used for supporting collaborative research and summer school teaching. For example, Jupyter notebooks could be used to provide worked examples of: i) simple execution using custom input data ii) pre-processing of in situ data, iii) creating and testing custom gas transfer parameterisations and pre-processing functions, iv) driving FluxEngine with a 580 custom python script to perform a sensitivity analysis, or v) using the verification tools module to verify custom changes and extensions to the toolbox.
Conclusions The FluxEngine is an open-source and freely available software toolbox that provides standardised and Collectively these improvements have streamlined the process for extending the toolbox and will allow users to easily take advantage of newly developed gas transfer velocity parameterisations and/or new sources of input data. These new tools and the toolbox are fully compatible with the internationally 600 agreed data structures being used by the SOCAT and the MEMENTO communities. The inclusion of the handling of CH 4 and N 2 O sea-air gas fluxes is intended to directly support those communities studying these gases. Significant international research focus and effort is now being directed to collating data on these gases towards monitoring and understanding their spatial distribution 605 and variability.
paper uses four case studies to illustrate key developments and extended capabilities now contained within version 3. 0 of the FluxEngine toolbox. Collectively the case studies illustrate user 125 selectable grids, support for calculating sea-to-air gas fluxes from time series data collected by fixed monitoring stations and research cruises (and how to incorporate the flux outputs into the original dataset to create a coherent time series), the ability to calculate nitrous oxide (N 2 O) and methane (CH 4 ) Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2019-45 Manuscript under review for journal Ocean Sci. Discussion started: 15 May 2019 c Author(s) 2019. CC BY 4. 0 License. sea-to-air gas fluxes, the addition of a new forcing variable (kinetic energy dissipation rate) and the ability to run ensembles of any of these calculations to characterise method uncertainties.
170 example, a simple Python script can be written to run a sensitivity analysis where ensembles of flux calculations are required without any need to modify the underlying FluxEngine software. Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2019-45 Manuscript under review for journal Ocean Sci. Discussion started: 15 May 2019 c Author(s) 2019. CC BY 4. 0 License. To provide an indication of the execution time a benchmarking analysis was performed using an Intel Core i5 5. 7 GHz Laptop processor with 8GB RAM running MacOS El Capitan. The automatic 175 installation took ~3 minutes to complete and the basic verification script using the Woolf et al. , (inreview) reference dataset (involving a global one year analysis of the gas fluxes for 2010, monthly temporal resolution and 1 ° × 1° spatial resolution) took approximately 6 minutes to complete.
to modify the core FluxEngine software, through copying and then completing the Python template function provided within the source code (data_preprocessing. py). Storing the completed function into the data_preprocessing. py file will then result in the custom pre-processing function being automatically available for use in any configuration files. 200These features make it possible to use any observational netCDF dataset by specifying the file path and, if required, appropriate pre-processing functions. For example a custom pre-processing function could resample the input files, followed by a transformation to change the projection. This flexibility is conceptualised by the diagram in Fig. 1. 205 Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2019-45 Manuscript under review for journal Ocean Sci.
input data specification to select in situ data files and unit conversion using preprocessing functions (Sect. 2. 2). Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2019-45 Manuscript under review for journal Ocean Sci. Discussion started: 15 May 2019 c Author(s) 2019. CC BY 4. 0 License. Utilises new support for in situ data analysis, including use of text2ncdf. py, daily temporal resolution, use of the reanalysis tool and output formatted as time-series (Sect. 2. 3). Case study 3: Surfactant suppression of sea to air N 2 O gas fluxes using the MEMENTO database.
465to N 2 O because of the sparsity of CH 4 data. In situ and 1° x 1° gridded monthly mean atmospheric and ocean partial pressure of N 2 O, sea surface temperature and salinity were obtained from the MEMENTO database for the Atlantic Meridional Transect (AMT) cruise (AMT-24, JR303), which took place between September and November 2014 (Brown and Rees, 2018). These data were supplemented with Earth observation wind speed, U 10 , from the CCMP dataset and modelled air pressure from the 470 European Centre for Medium-Range Weather Forecasts (ECMWF). All input data were gridded to monthly means with a 1° x 1° resolution. While sea surface temperature measured from pumped water samples collected at some depth are used here, recent AMT cruises (from 2016) have included an.
The 495 time series are shown in Fig. 6cfor SST, wind speed, atmospheric and aqueous N 2 O, and sea-to-air N 2 O flux. The net fluxes along the transect are generally small and in both directions. The overall mean flux was negative but small, -2. 4×10 -3 ± 2. 5×10 -2 g N 2 O m -2 day -1 (no suppression) and -1. 9×10 -3 (±2. 0×10 -2 ) g N 2 O m -2 day -1 (suppression), indicating in both cases a small net flux into the ocean. There was a flux suppression due to surfactants of 13% for the entire dataset, while there was an 500 overall (net) change in flux of -20% (reducing the flux to the ocean). Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2019-45 Manuscript under review for journal Ocean Sci. Discussion started: 15 May 2019 c Author(s) 2019. CC BY 4. 0 License.
Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2019-45 Manuscript under review for journal Ocean Sci. Discussion started: 15 May 2019 c Author(s) 2019. CC BY 4. 0 License. FluxEngine was then used to calculate monthly sea-to-air CO 2 fluxes, globally, for 2010. All inputs to FluxEngine were provided as monthly averages with a 1° x 1° resolution. The other input data were wind speed data from WAVEWATCH III re-analysis forcing field (WAVEWATCH III development group, 2016), sea surface temperature from Reynolds Optimally Interpolated Sea Surface Temperature 545 dataset (OISST, Reynolds.
195 to modify the core FluxEngine software, through copying and then completing the Python template function provided within the source code (data_preprocessing. py). Storing the completed function into the data_preprocessing. py file will then result in the custom pre-processing function being automatically available for use in any configuration files. 200These features make it possible to use any observational netCDF dataset by specifying the file path and, if required, appropriate pre-processing functions. For example a custom pre-processing function could resample the input files, followed by a transformation to change the projection. This flexibility is conceptualised by the diagram in Fig. 1. 205 Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2019-45 Manuscript under review for journal Ocean Sci.
Flexible input data specification to select in situ data files and unit conversion using preprocessing functions (Sect. 2. 2). Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2019-45 Manuscript under review for journal Ocean Sci. Discussion started: 15 May 2019 c Author(s) 2019. CC BY 4. 0 License. Utilises new support for in situ data analysis, including use of text2ncdf. py, daily temporal resolution, use of the reanalysis tool and output formatted as time-series (Sect. 2. 3). Case study 3: Surfactant suppression of sea to air N 2 O gas fluxes using the MEMENTO database.
540 Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2019-45 Manuscript under review for journal Ocean Sci. Discussion started: 15 May 2019 c Author(s) 2019. CC BY 4. 0 License. FluxEngine was then used to calculate monthly sea-to-air CO 2 fluxes, globally, for 2010. All inputs to FluxEngine were provided as monthly averages with a 1° x 1° resolution. The other input data were wind speed data from WAVEWATCH III re-analysis forcing field (WAVEWATCH III development group, 2016), sea surface temperature from Reynolds Optimally Interpolated Sea Surface Temperature 545 dataset (OISST, Reynolds.
In an attempt to shed light on the lost years of this population, we present and analyse here the first numerical simulations of the long-term dispersal of NWA leatherback hatchlings emerging from the FGS nesting beaches. These simulations are performed using STAMM. As bycatch and stranding data are more numerous and better distributed in the North Atlantic than in the North Pacific Ocean, the simulations performed here offer an excellent opportunity to further validate STAMM. In addition, comparison of our simulations with these of GL will allow us to evaluate if the habitats encountered by the juveniles in the Atlantic are more favorable than in the Pacific and could therefore be a part of the explanation why the NWA leatherback subpopulation is doing better than the West Pacific one.
Simulations of juvenile leatherbacks dispersal in the North Atlantic Ocean Technical setup The technical setup of our simulations is directly inherited from that of GL. In this case, we release simulated leatherback hatchlings off the FGS coast but use the same data sources as GL for surface currents, NPP and water temperatures. We also use the same trajectory simulation software, the same hatchlings release procedure, the same model parameters (except for P 0 as explained above) and the same model integration period (18 years). More precisely, surface currents (V c ) are taken from daily outputs of the GLORYS-1 (G1) reanalysis of the World Ocean circulation  [43]  performed by the Mercator-Ocean centre (http://www. mercator-ocean. fr) with the NEMO numerical ocean model (www. nemo-ocean. eu).
Individual trajectories are computed using the ARIANE Lagrangian trajectory simulation software (www. univbrest. fr/lpo/ariane) with a daily time step and turtle velocities given by Eq. (  1 ). For comparison purposes only, passive drift trajectories are also computed using ARIANE fed with G1 surface currents alone. Trajectories are computed over a period of 18 years which likely covers the whole pelagic juvenile stage. To perform 18-year-long simulations with 7-year-long forcing data sets, we simply loop the forcing fields until the last released turtle reaches the age of 18. v 0 1. 2 m s -1 T 1 T 1 = 24-1. 05 M 0. 5.
Timing of dispersal events The size of the smallest juvenile recorded in a given area is particularly informative as it provides an indication of the age of the youngest individuals present in this area. It shall thus be comparable to the age of the youngest simulated turtle entering this area. Interestingly, bycatch and stranding data gathered in various sites along the European/North African Atlantic coastline and in the Mediterranean Sea (Table  2 , more details in Additional file 3) clearly demonstrate early arrival of juvenile leatherbacks off Mauritania. The smallest individual observed there has a CCL of 62 cm, well below the sizes of the smallest individuals observed everywhere else along the European or North African coastline. This observation is consistent with the active dispersal scenario which features a fast pathway towards Mauritania.
Upper and lower age estimates are derived from the growth curves of Avens et al. [62]  and Jones et al. [41]  respectively. The midpoint of each estimated age range is indicated by a black tick mark Fig. 7  Arrival areas used to determine the age at which active turtles reach the different zones where stranding/bycatch data are available However, this simulated timing is established for individuals born on the FGS coast while the FGS nesting aggregation accounts for only half of the NWA leatherback subpopulation. The other half nests in the Caribbean area up to Florida, that is well downstream of the FGS nesting beaches. Our simulations indicate that FGS juveniles take about 1 year to arrive off Florida. One can thus reason that individuals born in Florida shall reach the eastern side of the Atlantic up to 1 year faster than those born in FGS.
Acknowledgements We thank  Mercator Ocean  (http://www. mercator-ocean. fr/)for providing the GLORYS-1 global ocean reanalysis,  Florence Dell'amico (Aquarium La Rochelle , http://www. aquarium-larochelle. com/conservation/le-centre-des-tortues-cestm), for providing the Bay of Biscay leatherback stranding data set, and the  Ariane development team  for making the Ariane software (http://stockage. univbrest. fr/~grima/Ariane/)freely available. Special thanks to our colleagues  Inna Senina  and  Patrick Lehodey  for countless, most useful, discussions on the modelling of habitat-driven movements in marine animals. We.
3. 1 Components and Functionality An important facet of the WA project is the availability of the user model data analysis and advanced functionalities as a cloud service rather than as standalone software. To meet the requirements, the WA system will be designed as a modular, mobile system, with autonomous sensor components, at best wireless capable sensors. Particular attention will be paid to always acting within the framework of the European GDPR. As a minimum, the following components are planned within the WA project (Fig. 3 ). Since various sensors will be used to collect the worker's data, a Central Processing Unit (CPU) cloud solution will be employed to process all the data. The WA-Tool calculates the overall worker's strain-level by combining all the available information and considering risk assessment according to Occupational Safety regulations.
The ChaLearn AutoML Challenge 1 (NIPS 2015 -ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, subject to limited computational resources. It was followed by one round of AutoML challenge (PAKDD 2018). The AutoML setting differs from former model selection/hyper-parameter selection challenges, such as the one we previously organized for NIPS 2006: the participants aim to develop fully automated and computationally efficient systems, capable of being trained and tested without human intervention, with code submission. This paper analyzes the results of these competitions and provides details about the datasets, which were not revealed to the participants. The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn.