dataesr/scientific-paragraphs-categorization huggingface dataset:
53 compared to 0. 69). When labels derived from the NUVr versus r -K method are used, the resulting F1-scores are consistently lower compared to the case where the original labels (sSFR) are used. B. 9. Which observables are useful to select quiescent galaxies? Heretofore, we have approached the question of the usefulness of different features (colours, magnitudes, etc. ) in terms of whether they are informative and whether their inclusion significantly improves the quality of our classification models The columns are as follows: (1) Redshift range in which the test was conducted; (2) information on whether photometric redshifts were used to restrict the dataset to source in the specified redshift range ('Yes' or 'No'); (3) precision P for quiescent galaxy selection when using labels derived from the NUVr vs.
It is worth noting that these analysis methods provide information about machine-learning models and how they make use of the input data, rather than the data itself. As such, the information provided is limited by what the learning algorithms were able to learn from the data, and it is quite possible that additional relationships exist between features and the target that the learning algorithm was unable to find. Thus, while features found to be important are highly likely to be useful for the selection of quiescent galaxies, features found to be unimportant (or less important) may nevertheless hold hidden information that other selection methods (e. g. template fitting) may find useful when selecting quiescent galaxies. B. 9. 1. Which single optical band is most useful? In Fig. B. 9 we show the effect of adding one optical band to the Euclid photometry set, when selecting quiescent galaxies in specific redshift ranges from the Int Wide catalogue using XGBoostClassifier, with no foreknowledge of redshifts.
In addition, Fig. B. 10 shows the improvement in F1-score due to the inclusion of each optical band. First, we examine feature importance as a function of redshift when only the Euclid data are available  (Fig. B. 9, top left). In this circumstance, there is no single, decisive broadband colour for the selection of quiescent galaxies at z < ∼ 0. 5. The lack of sensitivity to the 4000 Å break, or to emission bluewards thereof, results in models with relatively low F1-scores (see Fig. 8 ). In this regime, our XGBoostClassifier models assign a fairly similar importance value to each feature. Above z = 0. 4, the 4000 Å break becomes redshifted into the I E band, becoming potentially detectable via one or more of the Euclid broadband colours. In the range 0. 5 < z < 1. 5, the colour A99, page 32 of 36 I E -Y E is the most sensitive to the presence of the 4000 Å break, and our XGBoostClassifier models consider this feature to be the single most important for these redshifts.
B. 9. 2. Importance of long wavelength bands We repeat the process described in Appendix B. 9. 1, this time considering the addition of the W1, W2, or 20 cm radio band and related broadband colours. The data for these three bands is Despite being very sparse, each of the three bands provide a significant improvement in F1-score within the redshift ranges 0 < z < 1 and 1. 5 < z < 3. We find little or no improvement evident in the redshift range 1 < z < 1. 5. The usefulness of the 20 cm band for discriminating quiescent galaxies from star-forming galaxies is intriguing and somewhat surprising. Clearly, there is a correlation (or correlations) between the galaxy class and the presence of radio continuum emission. We speculate that this may be due to a subset of star-forming galaxies being detected in radio continuum, and/or the presence of radio-loud massive elliptical galaxies whose X-ray emission is below the COSMOS2015 detection threshold.
Acknowledgments. This work was supported by  Fundação para a Ciência e a Tecnologia (FCT)  through grants  UID/FIS/04434/2019 ,  UIDB/04434/2020 ,  UIDP/04434/2020  and  PTDC/FIS-AST/29245/2017 , and an  FCT-CAPES   Transnational Cooperation  Project. LB acknowledges financial support by the  Agenzia Spaziale Italiana (ASI)  under the research contract  2018-31-HH. 0. KIC acknowledges funding from the  European Research Council  through the award of the Consolidator Grant ID 681627-BUILDUP. AH acknowledges support from the  NVIDIA Academic Hardware Grant Program. AH also thanks colleagues  Jean Gomes ,  João Pedroso ,  Catarina Lobo ,  Tom Scott ,  Ana Afonso ,  Patricio Lagos ,  Israel Matute ,  Stergios Amarantidis ,  Jose Afonso ,  Rodrigo Carvajal , and  Ciro Pappalardo  for useful discussions or comments.
Thus, the impact of using hydrological models with students while they are learning can be significant. Sanchez et al. (2016)  showed that the use of a simple spreadsheet with real hydrological data had a significant and positive impact on the civil engineering curriculum. AghaKouchak and Habib (2010) also found significant learning gains for students using modeling tools in class. Nevertheless, the added value of using models in class is not automatic and straightforward. For example,  Marshall et al. (2015)  demonstrated that the same hydrological course offered using either (i) Microsoft Excel (Microsoft Corporation, 2019), (ii) MATLAB (2018), or (iii) the COMSOL Multiphysics software  (Zimmerman, 2006 ) (https://www. comsol. com/, last access: 30 December 2022) made no significant difference in student performances.
This moves toward reproducible hydrology  (Hall et al. , 2022)  and leads to the emergence of experiments of virtual laboratories  (Ceola et al. , 2015; Tarboton et al. , 2014) , opensource software  (Coron et al. , 2017; Slater et al. , 2019) , and open datasets  (Addor et al. , 2017; Irving et al. , 2018). What about open hydrological teaching? 1. 3 A review of modeling tools designed for teaching hydrological modeling The development of modeling tools dedicated to teaching hydrology began in the 1960s, with the pedagogic hydrological model ABC  (Fiering, 1967; Kay et al. , 1982; Burt and Butcher, 1986; Kirkby and Naden, 1988). Since the development of ABC, several software programs have been designed for teaching hydrology (see HESS Special Issue entitled "Hydrology education in a changing world",  Seibert et al.
, 2013). Elshorbagy (2005)  used a system dynamics approach based on the STELLA visual programming language  (Richmond et al. , 1985)   et al. , 2022) , such as being a "light version" of a model (e. g. , HBV-light), having an inability to import one's own data (e. g. , TUWteaching, https://webaapptuwmodel. shinyapps. io/TUWteaching/, last access: 30 December 2022), having an inability to access and modify the source code (e. g. ,  MINERVE; García Hernández et al. , 2020) , having an inability to manually or automatically calibrate the model parameters (e. g. , HBV. IANIGLA;  Toum et al. , 2021) , or being based on proprietary programming language (e. g. , VIC-ASSIST developed in MATLAB).
RS 1. 4 R, a language increasingly used by hydrologists, especially for modeling. The open-source programming language R is one of the most widely used languages in the hydrological community. It offers many open-source libraries useful, for example, for retrieving hydro-meteorological data, performing spatial analysis, and analyzing hydrological statistics. The whole workflow undertaken in hydrological studies can be done with R (see  Slater et al. , 2019) , which is very useful for practical reasons. The reader is asked to refer to  Slater et al. (2019)  for further details about the advantages of R for all the steps of the workflow and to the R Hydrology Task View (https: //cran. r-project. org/web/views/Hydrology. html, last access: 1 August 2022,  Zipper et al. , 2022)  for a complete list of R packages linked to hydrology.
The choice of hydrological modeling R packages is particularly large (see  Astagneau et al. , 2021 , for a recent review), providing a variety of solutions adapted to the diverse problems or case studies that can be encountered. Here again, the reader is referred to  Astagneau et al. (2021)  for further details about the packages and models available. In addition, R facilitates interdisciplinary work in the other fields of geosciences in which R is also used (e. g. ,  Bezak et al. , 2019 , who use the airGR package,  Coron et al. , 2017 Coron et al. , , 2022 , for hydrological modeling and the prediction of landslides). One of the strengths of R is its ability to incorporate geographic data and spatial analysis, such as in the use of the MODIS dataset, for example, for modeling of snow accumulation and melt  (Riboust et al.
e. , running at the basin scale with aggregated input) and that were recently incorporated into an R package (airGR;  Coron et al. , 2017 Coron et al. , , 2022)). To provide teaching material, the airGR developers set up an add-on package dedicated to teaching hydrology, named "airGRteaching". This package contains a graphical user interface, simple functions, and hydrology exercises. Since then it has been used for teaching and for hands-on projects in various universities and engineering schools (see, for instance, a master's degree project using airGRteaching:  Roux and Brigode, 2018). Since airGRteaching relies on the widely used GR models (see, e. g. ,  Perrin et al. , 2003, which  presents the widely known GR4J model) and on the airGR package, which has gained lots of interest over the past few years (see  Coron et al.
, 2017 , which presents the airGR package or the list of publications on the airGR website (https://hydrogr. github. io/airGR/page_publications. html#Use_and_mention_of_airGR, last access: 30 December 2022) that lists all known uses of or references to airGR), we believe that this tool can pave the way to developing new hydrology teaching initiatives, developing similar tools, and promoting hydrology to a more general audience. In this paper, after introducing the general concepts taught in hydrology, we present the main features of the airGRteaching package and introduce several exercises using this package. 2 Description of airGRteaching 2. 1 The rationale behind airGRteaching: a glance backward The GR models were initially developed in the 1980s by Claude Michel and his colleagues at Cemagref (that recently became Irstea and then INRAE).
The main objective was to design efficient models, starting from a simple structure and gradually adding complexity that proved useful for improving the model's predictive power  (Michel, 1983). This approach prioritized predictive power over explanatory models  (Shmueli, 2010) , finding justification for this from results obtained using large data sets and not from predefined concepts. This led to the development of a family of models that are usually used in lumped mode (i. e. , running at the basin scale with aggregated input). To disseminate their models beyond the Fortran programming community, a long time ago, the developers of the GR models proposed Microsoft Excel spreadsheets containing hydrological models, namely, the GR1A, GR2M, and GR4J models, as well as the CemaNeige snow accumulation and melt model (see next section for a description of these models), accompanied by a dummy dataset (https:// gitlab.
irstea. fr/HYCAR-Hydro/ExcelGR, last access: 20 July 2023). The rationale behind this approach was dual: easily providing the GR models to external users (researchers and consultants from France and abroad) and illustrating the hydrological concepts to students with the models developed https://doi. org/10. 5194/hess-27-3293-2023 Hydrol. Earth Syst. Sci. , 27, 3293-3327, 2023 in-house. The relatively high efficiency and low computational time requirements of these models made them easy to run with Microsoft Excel. In addition, the use of Excel macros enabled interactivity (e. g. , the possibility to automatically update simulations when parameter values are modified by users), and graphs were predefined. Later, the airGR R package was developed to propose additional GR models, and the airGRteaching R package was built as an add-on package of airGR.
The GR models and the airGR package To ease the implementation of the GR models,  Coron et al. (2017 Coron et al. ( , 2022) )  proposed the airGR package. Gathering seven hydrological models and one snow accumulation and melt model, airGR can be seen as a research tool, as an efficient way for its developers to share research results, and as a tool simple enough to be used by water managers. The hydrological models included in airGR differ in their complexity and time step, with a gradual increase in complexity as the time step decreases, and various application objectives: -GR1A  (Mouelhi, 2003; Mouelhi et al. , 2006a)  is an annual one-parameter model, used for water resources assessment  (Baahmed et al. , 2015; Kouassi et al. , 2012). It consists of a single equation relating the annual streamflow to antecedent annual precipitation and potential evapotranspiration.
-Static and dynamic functions, plot() and dyplot(). These functions take any of the objects produced by PrepGR(), CalGR(), and SimGR() as main arguments (to be chosen). Graph-tuning arguments are available but optional. The dynamic graphs show the observed and simulated discharge time series. The static graphs render a choice of graphs to be selected with the which argument. Dynamic graphs use the functionalities of the dygraphs package  (Vanderkam et al. , 2018). Many graphical outputs are available (see Appendix A and B). Figure  A1  provides a general overview of the precipitation and streamflow records to identify possible outliers and periods with missing data. Figure  A2  adds the simulated streamflow to the previous graph in order to have an overall view of the calibrated model behavior and provides graphical diagnostic tools to check whether the simulated streamflow hydrograph fits the observed streamflow hydrograph.
The graphical user interface Using the functionalities of the shiny package  (Chang et al. , 2022) , the airGRteaching graphical user interface (GUI) called with the ShinyGR() function allows one to use the GR models with no programming skills at all, thanks to an intuitive interface. The ShinyGR() function takes hydrometeorological data and the simulation period start and end as arguments. Additional arguments can be provided if snow is present. Data can be provided for several catchments, and the function offers the possibility to use different themes for the interface. The GR and CemaNeige models and the objective function are to be selected within the interface. Figure  1  presents a commented example of the interface. Several intuitive elements can be found. On the left side are the following: -"Choose a dataset" enables a dataset to be selected from those provided by the user to the function.
-"Choose a model" enables a model to be selected among the GR2M monthly model and the GR4J, GR5J, and GR6J daily models according to the time step of the datasets provided (models at other time steps are not included in the GUI) and to activate the CemaNeige snow accumulation and melt model for the daily models. -"Parameter values" enables the parameters of the models to be modified. The parameters proposed are automatically adapted to the chosen model and the ranges are predefined. Changing any parameter value causes a real-time update of the plots and displayed scores (see below). -"Automatic calibration" enables an automatic calibration to be performed by optimizing a chosen objective function (among the Nash-Sutcliffe efficiency (NSE) and the Kling-Gupta efficiency (KGE) and with a squared root, inverse, or no transformation of discharge).
This option has no effect on the "Model performance" panel. -Two buttons allow users to download the displayed plot in a PNG file format, which can be useful for a report for example (in order to ensure the tracking of the downloaded files, various information is automatically added to the file header: name of the dataset, name of the model, simulation period, and parameter values; see Appendix C), and the hydrometeorological data (including the simulation) in a CSV file format, to be used externally for further analysis or to be saved. Figure  2  presents the airGRteaching GUI "Modeling" panels (a-d) and "Summary sheet" panels (e-f). If R is not installed on the students' computers, it is possible to run the airGRteaching GUI online. Indeed, the graphical user interface is available at https://sunshine.
Data associated with airGRteaching Users are free to use their own datasets, but the airGRteaching package benefits from the airGRdatasets package  (Delaigue et al. , 2023a) , which contains a dataset of 19 different catchments located in France (Fig. 3  and Table 2 ). This dataset is a subset of the larger CAMELS-FR dataset  (Delaigue et al. , 2022)  and has been assembled to include various French hydro-climatic regimes, with 12 rain-dominated catchments, 1 rain-and snow-dominated catchment, 2 snow-dominated catchments, 2 Mediterranean catchments, and 2 groundwater-dominated catchments. Figure  4  shows the main characteristics of the catchment set. Catchment area ranges from 25 to 3917 km 2 , with half of the catchment set draining less than 686 km 2. The dataset is composed of both static geomatic and physiographic catchment indices and hydro-climatic time series (solid and liquid precipitation, potential evapotranspiration, air temperature, and streamflow time series).
Teaching hydrology with airGRteaching This section and the accompanying sections in the Appendix present tests based on the airGRteaching pack-age and designed to illustrate rainfall-runoff modeling, model calibration, evaluation, and robustness in hydrological classes. These tests are also available as a vignette in the airGRteaching package: users can thus recreate all these illustrations using their own datasets. Table  2. List of the 19 catchments in France included in the airGRdatasets package (ID: identification letters displayed in Fig. 3 ). 3. 1 Understanding rainfall-runoff modeling.
Automatic calibration Automatic calibration of model parameters is also possible in airGRteaching using the procedure described by  Michel (1991)  and by considering one objective function such as NSE or KGE. To do so, there are two options in airGRteaching: 1. clicking on the automatic calibration button in the airGRteaching GUI; 2. using the simple airGRteaching command-line functions (PrepGR() and CalGR(); see command lines in Appendix Listing D6). The calibration algorithm available in airGRteaching comes from airGR and is described in further detail in  Coron et al. (2017, Sect. 2. 3). Two distinct steps are included in the procedure: 1. A systematic inspection of the parameter space is performed to determine the most likely zone of convergence. This is done either by direct grid screening or by constrained sampling based on empirical parameter databases.
One step further: projects on flow reconstruction, forecasting, and climate change The basic manipulations of the airGRteaching package illustrated in the previous sections can also be used in more comprehensive hydrological teaching projects, presented in a vignette format in the package (example in Fig. 11 ) available in both English and French. These three projects deal with flow reconstruction (i. e. , producing simulated streamflow over periods for which records are missing), flow forecasting (i. e. , anticipating streamflow conditions for days ahead from given initial conditions), and climate change applications (i. e. , transforming climate projections into hydrological projections). These can be run as stand-alone projects with the dataset available in the airGRdatasets package or run on other catchments by importing the necessary hydroclimatic series.
3. Impact of climate change on streamflow regime. Using catchment-scale delta-change-derived future climate projections, this project aims at quantifying the impact of climate change on the flow regime of the Durance at Embrun catchment (see Fig. 13 ). The concepts addressed and the skills developed with this project are the (i) delta-change method, (ii) flow regime, (iii) bias correction, and (iv) impact of snow on the flow regime. Users of the airGRteaching package may also produce their own exercises as airGRteaching vignettes, based on the three examples provided. We believe that the proposed exercises and projects are a must if one wishes to learn hydrological modeling. They represent the core of many catchment-related studies. 5 Limitations and perspectives.
g. , with different discharge functions) and unit hydrographs, to help understand each compartment of a model. This is possible with the RS MINERVE software  (García Hernández et al. , 2020). Other limitations, as mentioned in Sect. 2. 3, are that airGRteaching only offers a limited set of modeling options, compared to airGR. This however could also be seen as a strength, as proposing too many options could be cumbersome from a user's perspective, and these limitations are therefore voluntary. Remote sensing data, other than meteorological or hydrological data, cannot be used in airGRteaching at the moment. In addition, the effect of land cover changes cannot directly be an asset to airGRteaching, as is the case in some physically based models. Finally, proper uncertainty exercises, apart from the calibration on different periods, have so far not been a part of this tool, which we see as a simple way of starting hydrological modeling.
Perspectives Exercises linking hydrology with other disciplines and scientific communities could be developed by the coupling of the airGRteaching package with other numerical tools and models. First, using actual global (GCM) or regional (RCM) climate model outputs as rainfall-runoff model inputs would illustrate the impact of climate variability or emission scenarios on catchment hydrology, linking climatology and hydrology. In a similar way, streamflows produced by the airGRteaching package could be used as inputs to hydraulic models to produce flood maps in teaching projects involving both hydrological and hydraulic skills. Finally, coupling airGRteaching with models of water uses (e. g. , water withdrawal models for drinking water or irrigation) would have interesting teaching applications.
Another valuable perspective is to use remote sensing data to perform data assimilation for hydrological forecasting by recovering realtime meteorological (e. g. , precipitation measured by rain gauges), hydrological (e. g. , streamflow observed from gauging stations), or even satellite data (e. g. , MODIS snow cover observations) and using these data as inputs of a rainfallrunoff model in the airGRteaching package, e. g. , with the airGRdatassim package  (Piazzi et al. , 2021; Piazzi and Delaigue, 2021). Such applications would illustrate the added value of assimilating hydro-meteorological data for better modeling in hydrology. Other exercises could be centered around uncertainties, through coupling the airGRteaching package with sensitivity analysis methods. Finally, the airGRteaching package could be used for the development of serious games devoted to hydrometeorological applications, aiming, for example, to discuss the issues of making better decisions when considering probabilistic forecasts  (Ramos et al.
For engineers working in consulting firms, it is often somewhere in between, depending on their experience and their background. The GUI is frequently used to avoid being bogged down in problems of form and to concentrate exclusively on the underlying concepts of hydrological modeling. The simplified code version allows a smooth transition to the more complex airGR code. For the general public, the aim is usually to introduce them using the airGRteaching GUI to one of the fields of hydrology, to help them understand what a model is, and to raise their awareness of applications such as flood and lowflow forecasting and global change. The introduction to computer programming is ideal to teach these notions to students. If students are to take this tool into their own hands, they must gradually acquire the concepts without difficulty.