dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Drinking water contamination is a major matter of concern for worldwide public health, with worldwide economic and social effects  (Daud et al. , 2017; Sharma and Bhattacharya, 2017; Gwimbi et al. , 2019; Turner et al. , 2021). This emphasizes the need for decision-support modeling tools that could provide 1) reliable predictions of the risk associated with water contamination in prospective scenarios and 2) optimized production settings to mitigate the impact of reported contamination. To this end, models may be of great interest provided a series of conditions are satisfied  (Doherty, 2015; Hermans, 2017; Doherty and Moore, 2020). First, that models should properly account for the processes governing the outputs of interest. Second, that an appropriate and reliable observational dataset constrains the parameters of importance.
, 2022). Though facilitated, the interfacing of parameter estimation and uncertainty quantification algorithms with complex models remains difficult for transport models, which are characterized by long computation times and numerical instabilities. Focusing on the widely reported risk of contaminant transfer from contaminated rivers to groundwater production units, we present a framework based on particle tracking as a fast and effective surrogate model for contaminant transport. The approach, initially presented by  (Cousquer et al. , 2018) , is made available in a newly developed Python module, TrackTools which will facilitate its replication. The module provides particle seeding capabilities and post-processing options that are valuable for analyzing drinking water vulnerability of production wells or drains to pre-defined sources of contamination.
The script facilitates exploratory parametric analysis, which can be useful to investigate the system response to different configurations. The interfacing with the PEST + + suite  (White et al. , 2020b)  is detailed on a real-world case study which paves the way for history matching, hypothesis testing, and optimization of decision variables, which are essential to support decision related to the definition of wellhead protection area and the optimization of production settings. The theoretical background and numerical tools related to the developed Python module are described in Section 2. A simple synthetic model is presented in Section 3 and a parametric study is conducted to investigate the driving factors of mixing ratios computed with this approach. In Section 4, the interest of the method is illustrated on a drinking water production site with a fully script-based approach from model setup to pumping optimization through parameter estimation.
β k = 1 for "strong" sources and 0 ≤ β k ≤ 1 for "weak sources" where a mixing occurs (Pollock, 2016). In the latter case, β k can be quantified from the cell water budget at the endpoint cell  (Cousquer et al. , 2018). This allows the method to account for mixing in the source aquifer cell, where the boundary condition (typically a river) is applied. The approach is now integrated in the TrackTools Python module. Our approach relies on the USGS MODFLOW6  (Langevin et al. , 2017) and MODPATH7 (Pollock, 2016)  programs to simulate fluid flow and transport processes, respectively. We rely on the FloPy module  (Bakker et al. , 2016)  for the processing of model input and output files. Pre-processing capabilities of the TrackTools module are provided in the ParticleGenerator class, which can handle: • Particle seeding around groundwater sinks, which can be described by a Python geometry or ESRI TM shapefile, • Adding, removing and merging particles into groups for FloPy and MODPATH7.
After the simulation of flow with MODFLOW and particle tracking with MODPATH, the TrackingAnalyzer class of TrackTools can be used to process particle pathline data and derive the values of mixing ratios at each groundwater sink. The MODFLOW cell-by-cell water budget file is also required to derive the source cell mixing ratio β k. Results can be provided in the form of a data frame and plotting options are proposed for the description of the origin of groundwater withdrawn in the series of sinks where particles were seeded. 3 Synthetic case.
Model description In order to illustrate the presented Python module, a synthetic 2D case is considered with a production well in an unconfined aquifer in interaction with a stream. The domain (3,750 × 5,000 m) is discretized with a 2D regular mesh (25 × 25 m cells) with a local refinement around the stream and the well (6. 5 × 6. 5 m cells). A Cauchy-type boundary condition is applied to the upper limit of the model domain with a stage of 40 m and conductance of 10 -3 m 2 s -1 , while a Dirichlet-type condition is prescribed to the lower boundary with a stage of 20 m. They are simulated with the General Head Boundary (GHB) and Fixed Head (FH) MODFLOW packages, respectively. The left and right domain boundaries are considered as impermeable. The stream is simulated with a head-dependent flux (Cauchy-type) boundary condition with the dedicated MODFLOW river package (RIV), with a head ranging from 35 m to 25 m from the upper to the lower boundary condition.
The stream conductance is set to 10 -3 m 2 s -1. Steadystate flow conditions are considered and the transmissivity is assumed to be independent of water level fluctuations, which corresponds to the Boussinesq assumption. The well is considered as fully penetrating and is located at position (x, y) = (1212, 1363). We consider both homogeneous and heterogeneous hydraulic conductivity fields, the heterogeneous cases being generated with an isotropic exponential variogram for log 10(k) defined by a sill of one and a nugget of 0. 1 for two different ranges of 200 and 500 m as presented in Figure  1. These fields are generated using the geostatistical python package GSTools  (Müller et al. , 2021). During the following simulations, the mean hydraulic conductivity value will evolve in heterogeneity patterns (Figures  1B, C ).
Case study 4. 1 Model description The method described above is embedded in a workflow designed for model-based decision making and applied to a Exploration of derivatives quality behaviour regarding the number of particles with the hydraulic conductivity K i ranging from 10 -5 to 10 -3 m s -1 and the increment ΔK set to (A) 0. 01 × K i and (B) 0. 1 × K i. complex real world case study. We describe the script-based modeling workflow and interfacing with the PEST + +, which provides an illustration of the interest of the method and facilitates the replication of the approach to other cases. All scripts are available on a GitHub ® repository. The well field is located in South-West France and is critical to the water supply for the city of Bordeaux  (Cousquer, 2017; Valois et al. , 2018; Delbart et al.
The model setup was fully script-based with the FloPy Python library  (Bakker et al. , 2016)  for the pre-and post-processing of MODFLOW6 and MODPATH7 input and output files. A 2D single layer was considered under the Dupuit-Forchheimer approximation to represent the aquifer of interest. The model domain was discretized by Gridgen  (Lien et al. , 2015)  with a quadtree grid with four horizontal refinement levels so that square cell dimensions range from 200 m to 12. 5 m (Figure  6 ). External boundaries have been defined from the regional groundwater levels as no-flow or 3 rd type head-dependent flow boundary condition. The streams and drains are simulated with head-dependent (Cauchy-type) flux boundary conditions and the wells are represented by sink terms in the aquifer cells corresponding to their location.
In order to consider contrasting settings while avoiding the burden of transient simulations, pseudo steady-state flow conditions are considered  (Haitjema, 2006; Moore and Doherty, 2021). This hypothesis is supported in the present case since the permeable aquifer responds quickly to changes  Cousquer (2017). The TrackTools module is used to seed particles around the two wells and the two drains and to derive the mixing ratio of the water withdrawn. It was found that Model domain with boundary conditions inferred from the regional groundwater flows. Mesh refinement is conducted in the area of interest around the production and observation wells and drains (see inset). The parameterization of hydraulic conductivity has been conducted with pilot points. simulated mixing ratios were relatively stable with 500 particles seeded around each production unit (wells and drains).
The observation data set is composed of hydraulic heads, drain discharge rates, and mixing ratios. The latter were derived by end-member analysis from HCO 3-and Ca 2+ concentrations  (Delbart et al. , 2021). In order to reduce run times, all model files for each of these surveys were setup in separate folders (Figure  7 ). Doing so, file operations during parameter estimation were limited to writing parameter files and reading output files. These operations were all conducted with the dedicated methods of the PyEMU Python package. The parameter estimation was conducted with the widely used Gauss-Levenberg-Marquardt Algorithm (GLMA) as implemented in PEST + + suite  (White et al. , 2020b). Parameter increments (DERINC) values were adjusted by trial and error. Best results were obtained with relative parameter increments of 15% for hydraulic conductivities and 10% for all the other parameters.
Results The history matching conducted with the GLMA converged in approximately 10 iterations (Figure  8A ). The fit between simulated values with their observed counterparts can be considered as satisfying for heads (Figure  8B ) and reasonable for discharge rates and mixing ratios (Figures  8C, D ). Measurements of drain discharge rates can be considered as reliable and the misfit may rather be explained by the static, linear relation that is considered with the MODFLOW drain package. In contrast, measurement of mixing ratios derived from concentrations by end-member analysis are prone to uncertainties  (Delbart et al. , 2021). The structural error of this simplified model is therefore strong and surely contributes to the misfit, but uncertainties in the observation data and historic operating variables are also important.
Discussion and conclusion In the context of drinking water contamination issues, we provided a decision-making tool to investigate the vulnerability of groundwater production units. The TrackTools Python module offers pre-and post-processing functions of particle tracking data simulated with MODFLOW and MODPATH. Mixing ratios are inferred from flow contributions of each contaminant source to the discharge rate of a groundwater sink considering particle velocities and mixing in source cells. This can be appropriate where a production well or drain is vulnerable to a boundary condition such as a river. However, the method is not relevant when the source of the contamination is diffuse, such as agricultural contaminants driven by groundwater recharge. In the latter case, forward particle tracking is more appropriate [see e.
It also presents the advantage to be didactic and can be used to represent contaminant transfer in a visual and potentially interactive manner, which is important for end-users. The method was implemented on a real world case study and is provided with a series a script for the interfacing with the PEST + + suite. The parameter estimation with the GLMA and optimization by linear programming illustrated the interest of the approach. Difficulties in reproducing observations can be explained by errors in the historic dataset (observations and operating variables) and model structural error. In spite of our efforts to obtain a robust linear version of the model, the methods based on the Jacobian matrix have shown their limitations. This advocates for the use of more robust ensemble methods such as the Iterative Ensemble Smoother (IES  (White et al.
The use of a paired simple-complex model approach  (Doherty and Christensen, 2011)  can be suggested, where the complex model would be based on the resolution of the advection-dispersion equation. Future work will also focus on improving the reliability of the simplified transport models by considering additional processes, such as dispersion. For this matter, various recent particle-based methods  (Noetinger et al. , 2016; Gouze et al. , 2020; Roubinet et al. , 2022)  and machine learning techniques  (Kang et al. , 2021; Zhou et al. , 2021)  could be considered in order to consider more realistic configurations while keeping the low computational cost of the forward models. As mentioned in the introduction, the optimal complexity level is to great extent, case-and purpose-specific, as it will depend on the purpose of the modeling exercise and the quality of the available observation dataset.
Thus, while the large Gaia parallax may be suggestive of the presence of a nearby star in the Gaia aperture (whether related to the event of not), it is difficult to infer anything about the lens from this measurement. In addition, we note that Gaia reports a RUWE value of 1. 89, probably indicating some form of contamination of the measurement. Interestingly, OGLE-2018-BLG-1212 was the subject of a Gaia alert, on 2018-10-05 08:38:24, as being a transient of unknown origin. Two of the Gaia points, at HJD ′ = 8396. 86 and 8396. 93, were just 3 days after the anomaly. However, there are only four significantly magnified Gaia points in total. Hence, these data do not help constrain the event. The CMD is shown in Fig. 12. There are no useful limits on ρ. The OGLE-III baseline object has [(V -I), I] base = (1.
KMT-2018-BLG-2164 KMT-2018-BLG-2164 is neither unambiguously planetary in nature nor is the planetary interpretation significantly preferred. That is, it has only ∆χ 2 = 4. 7 relative to the binary interpretation. Even if Gaussian statistics applied, the binary probability would be ∼10%. Therefore, it should not be "registered as a planet" in community databases, and we therefore refrain from trying to characterize it using Bayesian estimates. It is included in the present study only for completeness, that is, to identify all events with viable planetary solutions, regardless of whether these are unique.
OGLE-2018-BLG-1554 The case for a planetary interpretation for OGLE-2018-BLG-1554 is even weaker than for KMT-2018-BLG-2164. First, the A13, page 20 of 22 1L2S solution is slightly preferred by χ 2. Second, there is a competing binary solution at ∆χ 2 ≃ 0. Third, as we remarked in Sect. 4. 10, the measured θ E and µ rel for the planetary (but not binary or 1L2S) solution are highly unlikely a priori. Again, this event is only included in this study for completeness. We again counsel against its "registration" as a planet in community databases, and so we refrain from a Bayesian characterization.
We consider that the three entries below the line are unlikely to enter a mass-ratio function analysis, while five others  (OGLE-2018 -BLG-1126 , KMT-2018 -BLG-1025 , KMT-2018 -BLG-2004 , OGLE-2018 -BLG-1700 , and OGLE-2018 -BLG-1544)  will require detailed assessments. Here, we provide only the information necessary for these assessments but not the assessments themselves. All of the events above the line should be entered in planet databases, with names such as OGLE-2018-BLG-1126Lb, and none of the events below the line should be so entered. That is, while OGLE-2018-BLG-0100 is almost certainly planetary in nature, its degeneracies have not yet been delineated in published form, while the other two events below the line are not unambiguously planetary.
Acknowledgements. This research has made use of the KMTNet system operated by the  Korea Astronomy and Space Science Institute (KASI)  and the data were obtained at three host sites of CTIO in Chile, SAAO in South Africa, and  SSO in Australia. Work by C. H. was supported by the grants of  National Research Foundation of Korea  ( 2020R1A4A2002885  and  2019R1A2C2085965 ). W. Z. and H. Y. acknowledge support by the  National Science Foundation of China  (Grant No. 12133005 ). The  MOA  project is supported by  JSPS KAKENHI  Grant No. JSPS24253004 ,  JSPS26247023 ,  JSPS23340064 ,  JSPS15H00781 ,  JP16H06287 , and  JP17H02871. UKIRT is currently owned by the  University of Hawaii  (UH) and operated by the  UH Institute for Astronomy ; operations are enabled through the cooperation of the  East Asian Observatory.
Delphi procedure We conducted an online Delphi procedure approved by the Ethics Committee of the IRCCS San Camillo Hospital, Venice, Italy. Delphi is a structured procedure that utilizes a series of questionnaires to collect feedback from experts. The Delphi methodology leads to a consensus among worldwide experts on a given area of interest. The consensus is reached through a series of rounds in which data from each round are presented to the experts that are asked to provide their opinion round by round until consensus is reached  (Hasson et al. , 2000). The Delphi procedure was chosen because it has several benefits: i) it is implemented on the web; ii) it allows the inclusion of a large number of experts across different countries; iii) it allows communication via email; iv) it allows to keep participants' anonymous; v) it makes it possible to collect feedback through consecutive rounds in which the questionnaire is progressively revised by many experts; vi) it allows analysis and summarization of the data; vii) it reduces the possibility that an expert or a group dominate the process.
Afterwards, the questionnaire was implemented online in the Delphi decision Aid website and a uniform resource locator (URL) link to the website was created. Then, each member of the steering committee provided a list of experts to be contacted. Nominated experts were searched across the major research databases (Scopus and PubMed) to verify they had at least 2 publications in the field of TMS. A total of 113 international experts on TMS, from 85 hospitals or research institutions located in 29 different countries, were invited (Table  1 ). The experts were contacted through an invitation letter sent by email including the URL link to the questionnaire. Once they accepted to participate, they were given three weeks to provide their answers in each round. A reminder was sent to those experts who had not responded within one week.
A total of three anonymous rounds were performed. During the first round, the survey was individually completed (online) by each expert and data were downloaded into a database by a member of the steering committee (A. G. ). As the Delphi decision Aid website was disabled without any notice by the site administrator after the end of the first round, all the subsequent rounds were implemented in a Google Drive module and experts were provided with a new link to the questionnaire by email. Two members of the steering (A. G. , F. B. ) collected data round by round and prepared a data summary to be shared with all the other members of the steering committee. In round 1, experts were first introduced to the aims of the Delphi procedure. Then, the questionnaire was presented, and they were asked to indicate which items they would have excluded from the questionnaire.
Additionally, they were asked to provide any feedback and to revise current language forms, to merge items, or to propose new items for each section. Additionally, each expert had the opportunity to provide extended comments and suggestions in a dedicated text box. The questionnaire was then revised in accordance with data obtained by this round. In round 2, only the experts who completed the first round were contacted again by email and invited to fill out a new survey on a Google Drive module. In this round, experts were advised about items that had been removed after the results obtained at the first round, and about any other modifications. Additionally, a list of suggestions and comments that were proposed by the experts during the first round, for each section, was provided. Experts were asked to rank on a 5-point Likert scale their opinion about the inclusion of each suggestion embedded in the list.
A score of 5 indicated that the expert strongly agreed with the inclusion of an item in the questionnaire. Again, an open box was presented in each section of the survey so that experts could provide additional comments. Upon completion, data collected from round 2 were used by 2 members of the steering committee (A. G. , F. B. ) to further revise the survey. Experts that completed round 2 were contacted to participate in round 3. In this round, they were presented with the final version of the questionnaire (comprising all revisions), as well as with a summary of experts' comments provided in round 2. Round 3 also included the list of approved items from round 2. Again, a text box was available to report further comments and suggestions. At the end of the third round, each expert was invited to declare if she/ he approved or not the questionnaire in its final version, to reach a consensus.
After having completed the third round, the questionnaire, named TMSens_Q, and an original draft of the manuscript were publicized for a 4 weeks period on the IFCN website to collect suggestions from the wider scientific community. Collected suggestions and comments were submitted to the experts through a fourth round of the Delphi procedure. For this round, the 113 experts invited for the first round as well as 19 new experts and vendors from many TMS companies were contacted to gain a final consensus on the questionnaire and on the manuscript. Experts were given two weeks to provide their comments. The feedback from this last round was used for a final revision of both the manuscript and the questionnaire. To expand the content validity of the questionnaire, we asked 20 researchers currently conducting experiments with TMS, to complete a rating scale to evaluate the final TMSens_Q  (Martz, 2010).
Acknowledgements. 107 Funding. 107 Appendix A. Supplementary material. 107 References. 107 Acknowledgements We thank  dr. Tendler Aron  for reading the manuscript and providing his feedback. We are grateful to all panel members who participated in this Delphi consensus study:  T. O. Bergmann ,  D. Blumberger ,  S. Borgomaneri ,  L. Boyd ,  N. Brihmat ,  A. Brunoni ,  G. Campana ,  L. Carpenter ,  L. Cattaneo ,  G. Cona ,  A. D'Ausilio ,  R. Dubbioso ,  M. Feurra ,  F. Fiori ,  P. Fitzgerald ,  A. Flöel ,  K. Hoy ,  Kallioniemi ,  C. Licht ,  L. Meteyard ,  T. Mitsuaki ,  M. Nitsche ,  G. Pellegrino ,  M. C. Pellicciari ,  I. Premoli ,  N. Rogasch ,  S. Rossi ,  M. Scheckelman ,  C. M. Stinear ,  A. Tomeh ,  P. Turriziani ,  C. Urgesi ,  A. Valero-Cabré ,  D. Veniero ,  X. Xue. Additionally, we thank all the authors that gave their contribution through the IFCN website:  J.
Software Python is an interpreted language created in 1991 by Guido von Rossom. Python interpreters are available for most operating systems, and in particular available for the Rasbian Strech version of Linux that is installed on Raspberry Pi3. Python supports oriented-object programming. Free distributions of Python and of useful libraries for Python can have been found on the Internet. Among the libraries that are used for the work presented here are Numpy (for numerical work), Matplotlib (for graphical work), and OpenCV (for computer vision).
The DARE method Students have to organize the workshop using the pedagogical DARE method developed by the authors  [6]. DARE is the acronym for Discover, Apply and Resolve. This method can be perceived as a rewriting, for computer languages, of the classical three-steps tests developed for language teaching: first translating from foreign language to native language, then translating from native language to foreign language and last writing an essay in foreign language. The Discover step is devoted to translating computer language (here Python and OpenCV instruction) into native language. Workshop participants are given scripts they have to run and comment. In the Apply step, workshop participants have to answer very simple questions using the instructions seen in the first step: they simply translate basic works into computer language.