dataesr/scientific-paragraphs-categorization huggingface dataset:
The main idea of K-fold cross validation is that the entire interval of time-series is divided into a number of intervals. After that, one of the intervals is excluded and models are developed (hereafter referred to as "trained") on the remaining dataset and tested on the excluded time interval. Such a validation is repeated for each of the time intervals one by one. The value of the error is estimated as an average from all models trained in the manner as described above. The results of the models and their K-fold cross validation are shown in Figure  7. For nowcasting (estimation of the current conditions or short-term predictions), the simple models based on average values of Kp and values from the previous solar cycle turned out to be more accurate than the persistence predictions. Addition of the solar wind data resulted in a much more accurate prediction.
They also analyzed the input data and tested various ML methods to reduce the dimensionality of the input data, which in this case was historical measurement of the solar wind parameters. This procedure is usually referred to as "feature selection" in ML. As the predictions utilize time series, the number of input values for the training (in ML referred to as "features") grows when high cadence is considered, and the number of inputs may be very large when the long-term history is considered. Handling of such large input datasets may be computationally demanding and can result in so-called "overtraining" (or "overfitting"), which will decrease the accuracy. Moreover, it becomes more difficult to evaluate the importance of each of the inputs for the model. To find the most important subsets of inputs, Zhelavskaya et al.
Requirements for operational application To decide and establish the user requirements for operational application, a survey was sent to 19 samples, and 8 answers were obtained. The participants came from public, private, research and non-research institutions. The questions covered topics related to the distribution of the computational model, the information that the possible users think is of more interest, in which aspects space weather affected their operations or research, what interface would be more interesting for their applications, etc. Using the information drawn from this survey, the following user requirements were derived: The prototype models implementation shall be made available in the form of source code; The prototype models must be accessible from Python code; DTM and MCM shall be made available to the users with a single-entry point; The prototype library shall be made available for Linux systems; The prototype shall be disseminated in a website that allows running it from a web browser; The prototype shall allow using the models with minimal inputs (date, location and altitude); The prototype shall allow the users to optionally use their own space weather indices; The prototype library will reject erroneous user inputs; The models shall be integrated in the VSWMC framework; The prototype library models shall expose a unique atmospheric model comprising the DTM and MCM; The library will cover altitudes from 0 to 1500 km; The run-time performance of the library will be comparable to the performance of the isolated underlying models; The prototype shall be verified against results of the standalone models.
Conclusions In this paper, we report on progress with SWAMI to date. The aim is to produce a high quality whole atmosphere model that is suitable for operational use. The DTM has been re-developed using a more accurate and consistent neutral density observation database than has been used in the past. In addition, new data from Swarm-A, TLE global mean densities and the US Air Force EDR densities have been assimilated into a new operational model DTM2019_oper. Assessment of this model shows it is slightly more accurate than DTM2013 and that DTM as a whole maintains its advantage over other models like NRLMSISE-00 and JB2008. A research version of DTM, called DTM2019_res, has also been developed which uses Hp60 indices instead of Kp as a geomagnetic proxy, and F30 instead of F10. 7 as solar activity proxy.
The performance of this model over the entire observational dataset is good, and this reveals DTM's ability to represent the impacts of solar activity on the thermosphere. Assessment of the impacts of individual geomagnetic storms, and thus the impact of using Hp60 instead of Kp, is a more complex task that has yet to be completed. The UM has been extended from its original upper boundary of 85 km to run stably and accurately with a 135 km lid. Initial simulations did not employ any changes to the model to account for the MLT other than the use of a raised lid. It was quickly realised that use of a nudging scheme  (Griffith et al. , 2020)  to approximate MLT radiative and chemical heating was required, and initial runs used this, with the UM SOCRATES radiative heating switched off above 70 km.
At this stage the nudging scheme can be turned off. The availability of the UM up to 135 km means that code has been written to read, compare and blend UM output with DTM, which is the first step towards developing MOWA and MCM. This initial comparison has highlighted the inaccuracies in the UM mentioned above. The blending between UM and DTM will likely also be more accurate if the UM lid can be raised further, to around 150-170 km. Initial attempts to run the UM at these altitudes has required a very strong "sponge" layer or have failed. These can be addressed by introducing a molecular diffusion and viscosity scheme. This will introduce realistic damping and enhance model stability. The scheme has already been developed by  Griffin & Thuburn (2018)  but needs to be adapted to run with the UM dynamical solver.
Overall, this suggests the Hp indices are behaving in the way they were designed to. A test Hp dataset is now available for users. An open-ended version, not capped at Hp = 9, is under development to allow a more detailed description of extreme events. The development and testing of neural network and other machine learning methods applied to the forecast of geomagnetic indices is also an important part of SWAMI since this will lead to improved thermospheric forecasts for all the models used or developed in SWAMI. An initial assessment showed that for nowcasting, simple models based on average values of Kp and values from the previous solar cycle turned out to be more accurate than the persistence predictions. Addition of the solar wind data resulted in a much more accurate prediction. For predictions more than 12 h into the future, recurrence (values from the previous solar cycle) starts playing an important role.
Forecasts trained on the original Kp time series tend to systematically underestimate storm-time Kp data as the majority D. R. Jackson et al. : J. Space Weather Space Clim. 2020, 10, 18  of the data points used as an input are quiet-time values. Duplication (or "resampling") of the points corresponding to high Kp, in order to produce a rebalanced dataset for training, can lead to improved index forecasts during storm times, though at the expense of decreasing the accuracy during quiet times. Such tailoring should be the subject of future research. While the above tests provided a systematic error estimation for the models based on neural networks, it remained unclear if the accuracy could further be improved by utilizing different methods for regression. To address this question, the performance of the LR, GB and FNN machine learning methods were compared.
Shprits et al. (2019) utilized 1-minute propagated solar wind measurements from 2005 until 2016 obtained from the OMNI-Web data service. They first divided the considered time interval into 11 yearly periods and then performed a K-fold cross validation. Each cross-validation used one year that was excluded from training for validation and the rest of the data for training. Training and validation were repeated 11 times, resulting in 11 training errors and 11 validation errors. The average of all of these errors was used as an indicator for the average over a solar cycle of each of the models. For a more detailed description of the model parameters and datasets, please see Shprits et al. (2019).
Acknowledgements. The  SWAMI  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 776287. We thank the observatories contributing to Kp and the organizations that operate them for providing access to near real-time observatory data. These are BGS (for LER, ESK, HAD),  DTU Space (for BFE) ,  SGU (for UPS) ,  GA  (for CAN), GNS (for EYR),  USGS  (for SIT, FRD),  NRCan  (for MEA, OTT). NGK and WNG are operated by  GFZ. We thank  Fernando Pina  for contributing to the work on operational application. The editor thanks  Elco Doornbos  and an anonymous reviewer for their assistance in evaluating this paper. The editor thanks  Elco Doornbos  and an anonymous reviewer for their assistance in evaluating this paper.
The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017-2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data lead to the same conclusions: both the dynamics and the amplitudes of the soil moisture simulated with the SSF and SSF-DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. The opportunity of improving the two-layers model calibration is then discussed. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil moisture simulation during flash floods, with respect to both gridded data and local soil moisture measurements.
The MARINE model  (Roux et al. , 2011 ) is a distributed, physically based hydrological model. MARINE consists of three main modules: first, precipitation is separated between surface runoff and infiltration using the Green and Ampt model; then the subsurface flows are represented using an approximation of the Darcy's law; finally, the overland and river fluxes are simulated using the Saint-Venant equations simplified with kinematic wave approximation. Based on sensitivity analyses of the model  (Garambois, 2012) , five parameters are calibrated in MARINE for the representation of the soil and the surface: the multiplier coefficient for soil depth maps (C z ), the multiplier coefficient for the spatialized saturation hydraulic conductivity used in lateral flow modelling (C kss ) the multiplier coefficient for the spatialized hydraulic conductivity at saturation that is used in infiltration modelling (C kga ), and two friction coefficients for low and high-water channels.
The studied events In this work, the ANTILOPE quantitatives precipitation estimates (QPE)  (Champeaux et al. , 2009)  are used for precipitation estimation. The ANTILOPE-QPE are based on a fusion between the radar data provided by the operational radar network ARAMIS  (Tabary, 2007)  and the measurements at pluviometers, spatialised by krigging method. ANTILOPE-QPE precipitation are available on the hourly time step, at the kilometric resolution. The critized observed discharges at the outlet of the two catchments are taken from the hydrometric French database (www. hydro. eaufrance. fr). Table  1  presents the characteristics of the studied event. Three flash flood events are considered for each catchments over the 2017-2019 period. The heterogeneity of the studied events has to be noted: for the Orbieu catchment, the extreme event of October 2018 represents the historical maximum for this region, with well known dramatic damages to infrastructures and populations.
(  2019 ) and represent about 5% of the SMOS-IC SSM values. • The ESA CCI product provides surface soil moisture datasets at daily temporal time step and 25 km spatial resolution. In this product, the AMI-WS and MetOp/ASCAT/C-band data are merged with several radiometer soil moisture products, along the algorithm presented by  Wagner et al. (2012). The uncertainties associated with the ESA CCI SSM product is considered as the variance of the dataset, estimated through triple collocation analysis. Uncertainties represent about 3% of the ESA CCI SSM values. Figure  2  jointly displays the catchment average for these products over the studied events, as well as their respective fraction of missing values. The impact of the spatial resolution on the spatially averaged values can be clearly noticed.
Method for comparing gridded data to SMOSMANIA observations The SMOSMANIA observation network provides valuable information for the upper soil water content. However, it raises 280 the issue to compare point measurements to the gridded simulated soil moisture. Various strategies might be used to face this issue, among which averaging at a large time scale  (Tramblay et al. , 2010; Fuamba et al. , 2019). In this study, considering the fast-evolving processes involved, we choose to maintain the hourly time step for soil moisture analysis. The important spatial variability of the soil moisture is then taken into account by spatial averaging the gridded simulated values around the measurement point. In order to consider equivalent surfaces for the grids simulated in MARINE and provided by the LDAS-.
1. 2). For the simulations using the SSF-DWF soil model, the moisture of the surface layer is considered here; iii) the LDAS-Monde surface soil moisture HU surf for the 2. 5 kmx2. 5 km grid cell that contains the SMOSMANIA station; iv) the CGLS SWI when available for the 1 kmx1 km grid cell that contains the SMOSMANIA station for the Orbieu catchment. No data are available for the grid cell that contains the station for the Ardeche catchment. Table  5  provides the Kendall correlations associated with the hourly time series presented on figure  6. The values in bold are the best correlation values between the SMOSMANIA measurements and the MARINE outputs or the LDAS-MONDE HU surf for each event. Soil moisture simulated with the base model significantly differs from the simulations using the SSF and the SSF-DWF 350 models: the soil layer empties faster with the base model, leading to a simulated soil moisture significantly lower with the base model than with the two other models.
Overally for the simulated events, the simulated soil moisture and the SMOSMANIA measurements appears to be better correlated when using the SSF-DWF model rater than the base model or the SSF model. The soil physics used in the SSF-DWF, i. e. the use of the volumic soil water content rate and the vertical discretization into two layers, allows to enhance the soil moisture simulation for the surface layer, with respect to in-situ measurements. This point will be developed by considering the catchment average of simulated soil moisture in the next section. In addition, the soil moisture simulated for the surface layer with the SSF-DWF is globally higher than for the two other models. This behavior can be explained by the fact that, for the SSF-DWF model, soil depths are taken from the INRA soil data base, whereas for the base model and SSF model, a multiplicative, calibrated coefficient superior to 1 is applied.
Acknowledgements. This work was funded by the  STAE-IRT Saint Exupery fundation , in the framework of the  POMME-V  project. The  THEAI-Land  VHSR data are broadcast through the prism platform, available at www. theia-land. fr. The discharge measurements and the SIM soil moisture data were provided by the  French Service for Flood Prevision (SCHAPI). The precipitation data were provided by  MeteoFrance. The authors would like to thanks  J. P. Wigneron ,  M. Zribi  and  N. Baghdadi  for providing the SMOS-IC and THEAI-Land VHSR data, respectively, and feeding the discussion on these products.
Methods: We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms "conversational agents," "conversational AI," "chatbots," and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis.
Search Strategy We adopted methodological guidance from an updated version of the Arksey and O'Malley framework with suggestions proposed by Peters et al  [40]  in 2015 to conduct our scoping review. To identify literature pertaining to the application of conversational agents in health care, a broad literature search was conducted in April 2019 in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central. Given the novelty of the field, the amount of ongoing research happening in the area, and to increase comprehensiveness, we also searched for the gray literature in the OCLC WorldCat database, ResearchGate, Google Scholar, OpenGrey, and the first 10 pages of Google. We used an extensive list of 63 search terms, including various synonyms for conversational agents (Multimedia Appendix 1).
Inclusion and Exclusion Criteria To map out the current conversational agent applications in health care, we included primary research studies that had conducted an evaluation and reported findings on a conversational agent implemented for a health care-specific purpose. We excluded articles that just presented a proposal for conversational agent development, articles that mentioned conversational agents briefly or as an insignificant part of a review, as well as opinion pieces and articles where primary research was not conducted or discussed. A further point of exclusion was articles with poorly reported data on chatbot assessments where there was minimal or no evaluation data. In addition, we excluded articles concerning ECAs, relational agents, animated conversational agents, or other conversational agents with a visual or animated component.
Screening, Data Extraction, and Analysis Screening of articles for inclusion was performed in 2 stages: title and abstract review and full article review, undertaken independently by 2 reviewers. Following an initial screening of titles and abstracts, full texts were obtained and screened by 2 reviewers. From the included studies, 2 reviewers independently extracted relevant information in an Excel (Microsoft) spreadsheet. We extracted data on the first author, year of publication, source of literature, title of article, type of literature, study design and methods, geographic focus, health care sector, conversational agent name, accessibility of conversational agent, dialogue technique, input and output modalities, and nature of conversational agent's end goal. We piloted the data extraction sheet on at least five articles.
Potential discrepancies in the extracted data were discussed between the authors and resolved through discussion and consensus. We performed a narrative synthesis of the included literature and presented findings on (1) study specifics, such as study design, geographic focus, and type of literature; (2) conversational agent specifics (ie, conversational agent delivery channel, dialogue technique, personality, etc); (3) conversational agent content analysis; and (4) study evaluation findings. We used the principles of thematic analysis to analyze the content, scope, and personality traits of the conversational agents. Two researchers familiarized themselves with the literature identified, generated the initial codes in relation to personality and content analysis, applied the codes to the included studies, compared their findings, and resolved any discrepancies via discussion.
Search Findings The initial database searches yielded 11,401 records, and another 28 records were retrieved through additional sources such as the gray literature sources and screening of reference lists of relevant studies. A total of 196 duplicates were identified and removed, leaving 11,233 titles and abstracts that needed to be screened. Title and abstract screening led to the exclusion of 11,099 records, resulting in 134 full texts that needed to be assessed for eligibility. Of these, 87 articles were excluded, resulting in a final pool of 47 reports comprising 45 studies and 2 ongoing trials (Figure  2 ).
Galescu et al  [82]  described the CARDIAC system architecture including a knowledge base, task models, dialogue management, speech recognition, and language generation. Griol et al  [63]  presented a spoken dialogue system with specific details of the proposed emotion recognizer. For example, it considers pitch, frequency, energy, and rhythm of speech input from the user. Joerin et al  [75]  provided a less technically dense explanation for chatbot conversational agent development but mentioned technologies used in the process, such as emotion algorithms and machine learning techniques  [75].
Conversational Agent Evaluation Included studies that evaluated conversational agents reported on their accuracy (in terms of information retrieval, diagnosis, and triaging), user acceptability, and effectiveness. Some studies reported on more than 1 outcome, for example, acceptability and effectiveness. In general, evaluation data were mostly positive, with a few studies reporting the shortcomings of the conversational agent or technical issues experienced by users. Seventeen studies presented self-reported data from participants in the form of surveys, questionnaires, etc. In 16 studies, the data were objectively assessed in the form of changes in BMI, number of user interactions, etc. In 12 studies, there was a mixture of self-reported and objectively assessed outcomes and outcomes were not reported in the two ongoing trials (Multimedia Appendix 4).
Strengths and Limitations We conducted a comprehensive literature search of multiple databases, including gray literature sources. We prioritized sensitivity over specificity in our search strategy to capture a holistic representation of conversational agent usage uptake in health care. However, given the novelty of the field and the employed terminology, some unpublished studies discussed at niche conferences or meetings may have been omitted. Furthermore, although classification of the themes of our conversational agents was based on thorough analysis, team discussions, and consensus, it might not be all inclusive and may require further development with the advent of new conversational agents. In addition, although some conversational agents belong to more than 1 theme, we mostly classified them based on the dominant mode of application for the sake of clarity.
, 2003; Zabel et al. , 2012; Wang, T. et al. , 2015). LSM simulations without GCM coupling but forced by an atmospheric dataset (also called "offline" or "stand-alone" mode) do not allow feedback between the atmosphere and land surface. Therefore, errors in the simulated values solely arise from deficiency model structure/parameterization, uncertainty in the forcing data  (Yin et al. , 2018) , and mismatch in land cover between model and forcing data  (Zabel et al. , 2012). The foremost influential forcing factor on water cycle is precipitation  (Qian et al. , 2006; Decharme and Douville 2006) , although radiation and land cover (i. e. , vegetation) can also affect hydrological variables  (Dirmeyer, 2001; Guo et al. , 2006)  such as surface soil moisture (SSM) or evapotranspiration (ET), depending on the temporal scale  (Guo et al.