dataesr/scientific-paragraphs-categorization huggingface dataset:
The other operational streams were shown to be in good agreement, with a small bias of 2 mm in ZTD between the IGN and ENSTA-B/IPGP solutions due to the use of two different software packages. IWV estimates are made available for stations BCON and BCOS from all four operational streams since 31 October 2019. This offers the possibility to analyse the IWV time series both in near real time and on the long term. However, due to the use of various different ZTD to IWV conversion methods and auxiliary data, the uncertainty in the IWV estimates is variable and not optimal in these streams (see Table  4 ). It is planned in the near future to improve and homogenize the operational IWV conversion approach for all four operational streams and make these data available to the users. The GIPSY repro1 research stream includes 49 GNSS stations covering the Caribbean Arc (see Fig.
1 ). The ZTD and IWV estimates from this stream, which is the main focus of this paper, have been analysed for the period 1 January-29 February 2020 and made available with 5 minute time sampling for scientific applications. This stream used a slightly improved        Table  4. Separation of bias (mean) and random errors (one standard deviation) of IWV differences into contributions from ZTD differences, ZHD differences, and K differences, for two operational data streams (RGP daily and GIPSY rapid) and the reprocessed stream (GIPSY repro1). The three streams involve different auxiliary data for the ZTD to IWV conversion from PTU, TUV, and ERA5. See text for more details.
Data availability. The GNSS RINEX data used in this work are available from the following data servers: -RGP: ftp://rgpdata. ign. fr/pub/data/, last access : 29 January 2021; -UNAVCO: ftp://data-out. unavco. org/pub/rinex/, last access : 29 January 2021; -ORPHEON: http://reseau-orpheon. fr/, last acess : 29 January 2021; -SONEL: https://www. sonel. org/, last access : 29 January 2021. The ORPHEON GNSS RINEX data are provided for scientific use in the framework of the GEODATA-INSU-CNRS convention. The DOI references for the UNAVCO GNSS RINEX data are listed in Table A1 in the Appendix. The GIPSY reprocessed ZTD and IWV estimates from the 49 GNSS stations used in this study are available from AERIS, the French national data and service portal for the atmosphere (https://www. aeris-data. fr/, last access : 29 January 2021), under DOI https://doi.
| INTRODUC TI ON Artificial intelligence (AI) is defined as '… the science and engineering of making intelligent machines, especially intelligent computer programs'  (McCarthy, 1956). Increasingly sophisticated AI such as personalized advertisement and self-driving cars are revolutionizing a diverse range of professional sectors. In healthcare, AI is being adopted to aid healthcare professionals deliver high-quality care more efficiently and equitably. For example, AI can support less experienced healthcare professionals who may have fewer resources to still deliver high-quality care through learning from other's experiences (e. g. identification of rare disease symptoms through massive database searches)  (Schaefer et al. , 2020). In the context of nursing, examples of applications of AI demonstrate the potential impact that the use of these technologies can have in nursing practice.
ACKNOWLEDGMENTS This article is dedicated to  Dominique Le Quéau , who is deeply acknowledged for his scientific curiosity, enthusiasm and selfless support over many years. All this has been essential in promoting our laboratory astrophysics and interdisciplinary activities. We acknowledge  Jose Cernicharo  and  José Ángel Martín-Gago  for fruitful discussions, as well as  Anthony Bonnamy ,  Pavol Jusko ,  Ming Chao Ji  and  Michel Pellarin  for their contribution in the commissioning of the LVAP source. The authors also acknowledge support from  UAR Raymond Castaing of the University of Toulouse  and  Mr Stéphane Le Blond du Plouy  for the SEM observations.
Introduction: The incidence of pancreatic cancer is increasing worldwide and characterized by a particularly low survival rate. Studies have reported weak and inconsistent evidence for associations among reproductive factors, use of exogenous hormones, and pancreatic cancer incidence in women. Purpose: To investigate relationships between reproductive factors, exogenous hormones, and the rate of pancreatic cancer incidence in a large population-based prospective cohort of women in Norway. Methods: We used data from the Norwegian Women and Cancer study on 588 incident cases of pancreatic cancer diagnosed among 165,419 women, with mean follow-up of 18. 7 years. Cox proportional-hazard models were used to estimate HRs and 95% CIs for associations of interest. Results: Cumulative breastfeeding duration >24 months was associated with 63% decreased incidence of pancreatic cancer compared to no breastfeeding.
External validity in the NOWAC study is considered high, as the distribution of exposure is independent of the response rate and the cumulative incidence of cancers does not differ from national figures from the Cancer Registry of Norway. 37 The NOWAC study was approved by the Regional Committee for Medical Research Ethics and the Norwegian Data Inspectorate, and all participants enrolled in the study gave written informed consent. Women with prevalent cancer (n=6,696), death, emigration prior to date of inclusion (n=21), or those that reported extreme values in either questionnaire (n=336) were excluded from the analyses. Extreme values were defined for weight (<30 or >240 kg), height (<100 or >230 cm), age at menopause (<25 or >60 years), age at menarche (<8 or >20 years), and age at first birth (<12 or >50 years).
Acknowledgements We are thankful to Guillaume Martin and two anonymous reviewers for helpful comments, especially for suggesting to formally derive the offspring distribution and to alternatively solve our main problem by diffusion approximation. We also thank Matthew Hartfield for the critical reading of the manuscript. 7 Funding G AA = N AA,o + N AA,p + 2N AA,s (A1a) G Aa = N Aa,o + N Aa,p + 2N AA Aa,s + N Aa Aa,s (A1b) We assume that the number of exported pollen and the different numbers of seeds follow independent Poisson distributions. The different means depends on the fecundity of the parent, the viability of the seed produced and the proportion of each category: E[N Aa,o ] = 1 2 (1 -S)W ♀ Aa V Aa E[N Aa,p ] = 1 2 (1 -S)W ♂ Aa V Aa E[N AA Aa,s ] = 1 2 S 2 W ♀ Aa V AA E[N Aa Aa,s ] = 1 2 S 2 W ♀ Aa V Aa E[N AA,o ] = 1 2 (1 -S)W ♀ AA V Aa E[N AA,p ] = 1 2 (1 -S)W ♂ AA V Aa E[N AA,s ] = 1 2 SW ♀ AA V AA (A2) The factor 1/2 corresponds to the fact that the focal A is chosen with probability 1/2.
B. T. was supported by Erasmus Mundus Joint Masters Degree Scholarship awarded byErasmus+ programme. S. G. was supported by the Agence National de la Recherche (ANR SEAD-ANR-13-ADAP-0011). Symbol Meaning x i (t) Frequency of allele A in patch i at the start of the life cycle (generation t) x x ♂ i ♀ i i g ♂ X i Frequency of allele A in patch i after female fecundity selection Frequency of allele A in patch i after male fecundity selection Frequency of allele A in patch i after pollen migration Frequency of genotype AA at the start of the life cycle Y i Frequency of genotype Aa at the start of the life cycle X i Frequency of genotype AA after mating Y i Frequency of genotype Aa after mating X i Frequency of genotype AA after seed migration Y i Frequency of genotype Aa after seed migration N  *  i N i Population size in patch i 8 Competing interests The number of invading alleles in patch i Ñ Authors declate no competing interests.
Gas evolution in cultures was determined as indicated above. The purity of a selected isolate in culture, noted CAN, was checked after DNA extraction (as indicated below), followed by the amplification of the V3 hypervariable region of the 16S rRNA gene using the primer set 341F/518R for the Bacteria domain  [37]  to verify the absence of bacterial DNA amplification, then with the primer set A109F/1492R for Archaea domain  [38, 39]  and then sequenced by GATC-Biotech (Konstanz, Germany). The 16S rRNA gene sequence of the CAN strain (KR349725) was compared with those present in the NCBI non-redundant database, using the BLASTn search tool  [40]. A selection of representative homologous sequences was then aligned with CAN 16S rRNA gene sequence using MUSCLE [41], and phylogenetic trees were constructed and evaluated using the maximum-likelihood method  [42]  implemented in MEGA7 software  [43].
[11]. The abundance of targeted genes was reported as copy numbers per L for water or g for sediment. Bacterial and archaeal 16S rRNA gene V4 hypervariable regions were amplified by PCR using the 515F/806R universal primer set  [48] , with a barcode on the forward primer, as previously described by Dowd et al. , and were sequenced by the MiSeq Illumina platform of the Molecular Research Laboratory (Shallowater, TX, USA)  [49]. Sequence data were processed using MR DNA analysis pipeline (MR DNA, Shallowater, TX, USA). In summary, sequences were joined, barcodes were removed, then short sequences (<150 bp) and sequences with ambiguous base calls were removed. Sequences were denoised, then Operational Taxonomic Units (OTUs) generated, and chimeras removed. OTUs were defined by clustering at 97% of similarity.
Funding: This research was funded by the  French Institute of Research for Development (IRD) , the  INTERVIE CNRS INSU program , the  H2NAT  project supported by the  Midi-Pyrénées Observatory (OMP, Toulouse)  and  ANR MICROPRONY , grant number  19-CE02-0020-02. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
To derive an optimal observation system for surface ocean pCO 2 in the Atlantic Ocean and the Atlantic sector of the Southern Ocean, 11 observation system simulation experiments (OSSEs) were completed. Each OSSE is a feedforward neural network (FFNN) that is based on a different data distribution and provides ocean surface pCO 2 for the period 2008-2010 with a 5 d time interval. Based on the geographical and time positions from three observational platforms, volunteering observing ships, Argo floats and OceanSITES moorings, pseudo-observations were constructed using the outputs from an online-coupled physicalbiogeochemical global ocean model with 0. 25 • nominal resolution. The aim of this work was to find an optimal spatial distribution of observations to supplement the widely used Surface Ocean CO 2 Atlas (SOCAT) and to improve the accuracy of ocean surface pCO 2 reconstructions.
The data density is not homogenous, with southern latitudes being less well sampled in space and time  (Monteiro et al. , 2010). Sparse data coverage and the lack of observations covering the full seasonal cycle challenge mapping methods and result in noisy reconstructions of surface ocean pCO 2 and disagreements between different models  (Denvil-Sommer et al. , 2019; Rödenbeck et al. , 2015). The ship-based sampling effort is progressively complemented Published by Copernicus Publications on behalf of the European Geosciences Union. by autonomous observing platforms, such as biogeochemical ARGO floats equipped with pH sensors. The expansion of the observing system to autonomous platforms is of particular relevance in regions that are undersampled either because of the presence of fewer regular shipping lines (e.
A series of observation system simulation experiments (OSSEs) were carried out in a perfect model framework using output from an online-coupled physicalbiogeochemical global ocean model at 0. 25 • nominal resolution. Since all fields used by the feed forward neural network (FFNN) are produced by the same model run and thus internally consistent, the comparison between reconstructed and modelled pCO 2 distributions allows for assessing the theoretical skill for each experiment. Starting from measurements extracted from the SOCAT database, the goal was to identify how and where the new data from biogeochemical ARGO floats can improve surface ocean pCO 2 reconstructions and how to optimally integrate them with other existing platforms. Pseudo-observations were obtained by subsampling model output at sites of real-world observations.
Data and methods Here we present the ensemble of observing platforms that either already perform measurements to estimate pCO 2 or have the possibility to be equipped with new sensors to provide biogeochemical measurements  (Williams et al. , 2017). These datasets provide information on geographical, as well as temporal, positions and hence the distribution of pCO 2 measurements. In this section we also describe the ocean model output and how we use it in the OSSEs. As mentioned in the introduction, the data from the model co-localised with real positions of observing systems are called pseudoobservations.
Observing platforms Three observing platforms were selected for the study: (1) volunteering observing ships providing in situ measurements of surface ocean CO 2 fugacity (f CO 2 ), (2) moorings (OceanSITES), and (3) profilers (Argo). These observations form the dataset of geographical and temporal positions for our experiments. Surface ocean measurements of f CO 2 from multiple platforms are converted to pCO 2 and compiled in the SOCAT database  (Bakker et al. , 2016). Moorings are not routinely equipped with sensors of CO 2 fugacity. We used their geographical positions to identify possible locations for additional measurements. Biogeochemical ARGO floats are increasingly equipped with pH sensors, allowing computing pCO 2 from pH and SST-based alkalinity. For the design experiments, we considered distributions of physical ARGO floats  (2008) (2009) (2010) (2011)  from  Gasparin et al.
(2019)  and supposed that they were equipped with pCO 2 sensors. 1. SOCAT database v5. The database provides a good coverage of the Northern Hemisphere  (Bakker et al. , 2016;  https://www. socat. info/index. php/data-access/, last access: 20 February 2018). Data for the period 2001-2010 were used, representing ∼ 60 % of data in SOCAT database (Fig. 1a ). The use of data for the period 2001-2010 allows us to capture interannual variability from a long historical record of SOCAT data and to explore how SOCAT data can be enhanced by other observational platforms. It also provides more data for the train-ing of the neural network. While the data from 2001 to 2010 are used in training, the reconstruction focuses only on the years 2008 to 2010. We used the synthesis files SOCATv5, these are the raw data from which the gridded SOCAT product is derived.
There are 24 moorings in SOCATv5 that provided CO 2 fugacity measurements between 2001 and 2010. These moorings were excluded from OceanSITES data (see below). 2. Argo profilers. We used the network of Argo  (Gould et al. , 2004; Argo, 2000)  distributions provided by Mercator Ocean (details can be found in  Gasparin et al. , 2019)  for the period 2008-2010. This network provides a synthetic homogeneous distribution of one profiler per 3 • × 3 • grid box per 10 d, amounting to 310-360 measurements per day (Fig. 1b ) based on real trajectories of Argo floats. This synthetic Argo distribution was built based on the time, date and location of Argo profiles during the 2009-2011 period  (Gasparin et al. , 2019). To provide a homogeneous coverage  Gasparin et al. (2019)  removed some float trajectories in wellsampled regions, for example the Gulf Stream, or added floats in the low-sampled tropical and South Atlantic regions.
OceanSITES. This dataset combines observations from open-ocean Eulerian time series stations providing data since 1999 (Fig. 1c ). We used all available locations of moorings (except moorings included in SOCATv5) and added this information to the period of reconstruction, i. e. 2008-2010 (http://www. oceansites. org/, last access: 20 February 2018). It provided 318 additional positions to our dataset. For this study, the same set of predictors was used as in  Denvil-Sommer et al. (2019)  for training the machine learning (ML) algorithm: sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), mixed-layer depth (MLD), chlorophyll a concentration (Chl a) and atmospheric CO 2 (pCO 2,atm ). These variables are known to represent the main physical, chemical and biological drivers of surface ocean pCO 2  (Takahashi et al.
Model output and pseudo-observations Here we used the numerical output from an online-coupled physical-biogeochemical global ocean model, the Nucleus for European Modelling of the Ocean (NEMO)/PISCES model, at 5 d resolution. This configuration of the NEMO framework was implemented on a global tripolar grid. It coupled the ocean general circulation model OPA9  (Madec et al. , 1998) , the sea ice code LIM2  (Fichefet and Maqueda, 1997)  and the biogeochemical model PISCESv1  (Aumont and Bopp, 2006). Information on the simulation is given in  Gehlen et al. (2020)  and  Terhaar et al. (2019) , including the evaluation of the modelled mean state and the seasonal cycle of sea surface temperature and sea-air fluxes of CO 2  (Gehlen et al. , 2020). The geographical and time positions identified from the data mentioned before were used to create pseudoobservations by sub-sampling NEMO/PISCES model output at sites of real-world observations.
Thus, the positions of SO-CAT, Argo floats and mooring stations were chosen over 5 d centred on the NEMO/PISCES date and sub-sampled on the model grid. 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, it is counted as one measurement. No Argo floats were added to grid cells if there was already a measurement identified in the SOCAT database. All predictors and target pCO 2 were taken from model output at corresponding coordinates. These outputs served as the reference for validation and evaluation of our experiments and for assessing the ML method's accuracy. The simulation covers the period 1958 to 2010; the last 3 years were retained for the design study.
Observational system simulation experiences Table  1  summarises experiments designed for different combinations of observing platforms. The first test is based on individual sampling data extracted from the SOCAT database. As mentioned before, these data provide a good coverage of the Northern Hemisphere. The lesser coverage in the Southern Hemisphere results in a larger dispersion of methods based on these observations only  (Denvil-Sommer et al. , 2019; Rödenbeck et al. , 2015). This has motivated experiments with additional data from Argo profilers limited to the Southern Hemisphere. An experiment based on the full physical ARGO network was included to evaluate the method for a high spatial and temporal coverage (an optimal, yet unrealistic case). We have tested combinations of SOCAT data and (1) total Argo data, (2) Argo only in the Southern Hemisphere, and (3) 25 % or (4) 10 % of the initial (total) Argo distribution.
Finally, these experiments were repeated with additional mooring data. It is worth noting (Table  1 ) that OSSE 4 is closest to the target of the BioGeoChemical (BGC)-Argo program, with a BGC-Argo density corresponding to 25 % of the existing Argo distribution. However, we decided to choose OSSE  3 as a benchmark against which to evaluate individual experiments. This experiment has a high data density and provides additional information on a potential future BGC-Argo network.
). A sub-set of 50 % of data was used for training. A total of 25 % participated in the evaluation of the model during the training algorithm, and 25 % were used to validate the model after training. These data were chosen regularly in time and space: every third grid point was kept for evaluation, and every fourth grid point was kept for validation. Tables S1 in the Supplement presents the numbers of training data for each month and each OSSE. To adjust the number of FFNN parameters/weights we followed the empirical rule that suggests limiting the number of parameters to the number of training data points divided by 10 to avoid overfitting  (Amari et al. , 1997). The FFNNs for all OSSEs except OSSE 2 have four layers (two hidden layers) with 1116 parameters in total. The input layer has 15 input nodes and 20 output nodes that represent the input for the first hidden layer.
The first hidden layer has 25 output nodes, and the second hidden layer has 10 output nodes. The OSSE 2, which is based on Argo data for the period 2008-2010, has significantly fewer data for training, and thus the FFNN for the OSSE 2 is different: three layers (one hidden layer with 20 input and 10 output nodes) with 541 total parameters. All data have to be normalised before their use in the FFNN, as exemplified for SSS: SSS n = SSS -SSS SD(SSS). (2) SSS is the total mean of variable SSS, and SD(SSS) is standard deviation of SSS. Normalisation is required to rank all predictors on the same scale and to avoid the possible influence of one predictor with strong variability  (Kallache et al. , 2011). Following  Denvil-Sommer et al. (2019)  we normalised the geographical positions (lat, long) in the following way: lat n = sin(lat • π/180), long n,1 = sin(long • π/180), long n,2 = cos(long • π/180).
A K-fold cross-validation was used to evaluate and validate the FFNN architecture. The cross-validation is based on K = 4 different subsamples where 25 % of independent data are chosen for validation. In each of the four cases, 25 % of the data are different and there is no overlap. Thereby, each run has four outputs. Different architectures of the FFNN were tested and the final one was chosen based on skill assessed by the root-mean-square difference (RMSD), the r 2 and the bias of four outputs for each architecture. To ensure a good accuracy of the method and check that there is no overfitting, we compared the RMSD, r 2 and bias estimated from the validation dataset with those estimated from the training dataset. Denvil-Sommer et al. (  2019 ) provide a detailed description of the model, including the accuracy of the ML method and its ability to correctly reproduce the pCO 2 variability.
Diagnostics The comparison between OSSEs is done per biome, following  Rödenbeck et al. (2015)  (Fig. 2 , Table  2 ). Biome 8, North Atlantic ice, has been omitted due to poor data coverage in all OSSEs. It is expected that reconstructions over this region will yield large biases susceptible to interfere with the interpretation of results from individual OSSEs. In order to simplify the comparison, we used Taylor and target diagrams with standard deviation, biases, correlation  uRMSD = mean pCO 2 OSSE -pCO 2 OSSE -pCO 2 NEMO -pCO 2 NEMO 2. ( 3 ) For each OSSE and each output of the k-fold crossvalidation, we estimated a time mean difference between its pCO 2 and NEMO pCO 2 at each grid point: Diff j,i = mean T (pCO 2 OSSE j,i -pCO 2 NEMO ) = 1 T T t = 1 (pCO 2 OSSE j,i,t -pCO 2 NEMO t ), where mean T is a time mean over the period, T is a number of time steps, j is an index of the OSSE and i is an index of output from 1 to 4.
14 (-0. 66) µatm for OSSE 3 (Tables  3, 4 ). Over biome 12 all OSSEs show SD values lower than the one computed for NEMO/PISCES model output (Table  3 ). This could result from the SD of the mean output being slightly lower than the individual SDs for four OSSE FFNN outputs (not shown). However, individual SDs also underestimate the NEMO/PISCES SD, which might suggest that the ensemble of predictors does not properly represent the variability over the equatorial Atlantic. Reconstruction skill scores are improved by the addition of data from mooring stations to OSSEs 6, 7 and 8 in OSSEs 9 (hexagon), 10 (star) and 11 (triangle centroid) (Fig. 3  and 4 , Tables  3  and 4 ). Over the ensemble of eight biomes the decrease in the number of Argo data goes along with a general decrease of correlation coefficients, i.