dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012) , which was developed to perform reflexive studies on modelling and simulation activities in general. The second challenge was related to the fact that the outcomes of companion modelling approaches may be very difficult to measure, being both contextand time-dependent. In order to take up this second challenge, we relied on experimental economics  (Falk and Heckman, 2009)  which addressed the issues of reproducibility, context dependency, etc. Here, we transpose the conditions of a ComMod process into the laboratory by using the Minsky triad conceptual framework and the principles of experimental economics. To this end, we developed a software device based on the NetLogo platform and on the generic watershed modelling kit Wat-A-Game (WAG). This device, which we called AnaWAG for "Analyse Wat-A-Game" is available on the ComSES platform.
, 2010). We focused in this study on the first modelling loop during which the first model called KatAware was built. We refer to it hereafter as the KatAware triad that we called T. In the KatAware triad T , the object A was the whole Kat river sub-catchment, the observer B was a group composed by the members of a recently created water users' association at the level of the Kat river. The question that the group of stakeholders B had about the Kat river subcatchment was as follows: "How can a catchment action plan in the Kat river Valley be collectively designed?" and more precisely: "How should water use licences be attributed to water users and at what price?". In order to answer M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT to these questions, the group, B, built the first version of the agent-based KatAware model, which we called A * B.
The NRM situation At one moment of the process, the NRM situation (A) is specified by a scope defined (more or less strictly) by either a research project, or a local development project, or a combination of both. In our case study, the Kat river ComMod process, the NRM situation was scoped by the research-action project called "a stakeholder driven process to M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT develop a catchment management plan for the kat", founded by the South African Water Research Commission. The project established the area as the Kat river quaternary sub-catchment of 1700 km 2 including 19 km 2 of irrigated area and home to around 50'000 inhabitants in 2001. Irrigation took up by far the majority of the water in the catchment and relied mostly on the Kat Dam situated in the upstream section of the catchment, with a 24.
When RPG models are used, stakeholders are invited to play their own roles in order to represent and discuss scenarios that can happen in their NRM situation  (Barreteau, 2003a). The version 1 of the KatAware model, which we focused on in this study, is an ABM. It is extensively described in  (Farolfi et al. , 2010). and c ) the nature of the group of stakeholders, the involvement in the ComMod process of the group of stakeholders (relation ρ m ) can have several dimensions. For this relation, we assumed four non-exclusive possibilities of action and four non-exclusive possibilities of intention. The actions may be either: • facilitating the modelling and simulation activity and providing the modelling language (the individuals, usually the facilitators, provide a language to build the model and facilitate the process); • building the model (the individuals take part in the modelling process) • simulating the model by role-playing (the individuals take part in a participatory simulation of an RPG model, where they are invited to play a role); • simulating the model by changing parameters and observing simulation results.
A question about the ComMod triad The objective of our approach was to use models, sensu Minsky, to reflect upon ComMod processes. We may wish to raise numerous questions about ComMod triads, but we have very little visibility in terms of defining the limitations of the type of question that can be addressed using models based M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT on social experimentation. Computer models have been built and used to address questions about some aspects of the ComMod triad. For instance, an agent-based model was designed by Emmanuel Dubois to assess how player attitudes can change during a role-playing game, depending on the game settings  (Dubois et al. , 2013). However, the impact of the participatory modelling activity has never been explicitly addressed by the use of a model.
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT which an activity card stands, the owner of the land plot cards can choose to extract water pebbles from the river in order to perform the activity and gather the resources generated by that activity. At the end of the turn, players can change the activity cards standing on the land plot cards they own. For this study, we computerised the Wat-A-Game as a network game coded in the NetLogo Agent Base Modelling and simulation platform so that we could control the information available to the players. Figure  4  presents our conceptual model of the NRM situation on the left, and the graphic interface of its implementation as a computerized Wat-A-Game RPG. The details and implementation of the WAG RPG are described in Section 4.
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT In Agent-Based-Modelling in ecology or environmental science, modellers have a more conceptual understanding of a model, whereby a meta-model is a set of concepts that constitute a generic model, general enough to be specialized in less abstract (more specific) representations of the system under study  (Treuil et al. , 2008). In this paper, and following the general framework of the Minsky triad, an object was considered as a meta-model as long as it was used in our reasoning to represent another model used for a specific purpose, by a specific observer. With this definition, we considered an object as a meta-model only if we also modelled the observer, the object modelled, and their interactions. This definition was particularly suited to the study of ComMod processes, where we assumed that the object modelled (the NRM situation) was modified by the use or creation of the model by the observers.
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT can imagine an experimental setting where subjects would follow a complete ComMod loop: a first phase where players play, a second where they build a model and a third where they play again. However, the different possible situations in the first phase would have distilled the results and such an experimental setting would require many more observations. In fact, the Minsky triad conceptual framework should be used when a model of a ComMod process is built. Thus, the motivations that we can imagine are the same motivations as those that researchers can have to build a model. Varenne (2018)  organises the different functions that a simulation model can have in 5 categories: i. to ease experimentation, ii. to ease comprehensible formulation, iii.
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT the results and demonstrations made, will remain theoretical and will need to be confirmed by studies in the field on real ComMod processes. We consider our model of the ComMod triad as a lauchpad for thinking about ComMod processes. In a theory-building perspective, the more detailed our models of the ComMod triad are, the richer will be the discussions that we can have about them. For instance, a salient question about ComMod processes is the way they interfere with existing institutions (formal or informal) that frame the actions and perceptions of stakeholders. We plan to continue developing the AnaWAG platform in order to use T * models to represent these institutions. We are confident that this task is possible because the WAG modeling kit proposes a way of representing such institutions  (Abrami et al.
Software and data availability The model code and data used is published on the ComSES platform  (Bonté et al. , 2019)  with the documentation presented in Appendix D below. All the sotfware packages used are free of charge and open-source (Platform: Net-Logo 5. 3. 1, Programming Language: NetLogo, Operating System: Platform Independent, Model code licensed Under: GNU GPL, Version 3). NetLogo software is authored by Uri Wilensky (Email: uri@northwestern. edu. Phone: 847-467-3818. Fax: 847-491-8999. Offices: Annenberg Hall 337. CCL Lab Phone: 847-467-7593. Ford Lab Phone: 847-467-2838). The AnaWAG platform has been programmed under the NetLogo software by Bruno Bonté and Mamadou Ciss Diallo who are authors of this paper (see contact information in the authors' section).
5 on an area of 500 arcmin 2. The deep sample of the VVDS used the low resolution spectrograph LRRED covering the range 5500≤ λ ≤9350 Å. This permits us to observe important spectral features like the [OII]3727Å emission line from z∼0. 5 to z∼1. 5. For the ultra-deep sample, both blue and red gratings (LRBLUE and LRRED) were used to cover 3650≤ λ ≤9350 Å. The data reduction was carried out using the VIPGI software  (Scodeggio et al. 2005) , while the redshift measurements were performed with the EZ software  (Garilli et al. 2010). Each redshift measurement is accompanied by a quality flag indicating the probability of the redshift to be correct. The flag system consists of six different flags. Flags 2, 3, 4, and 9 (for objects with a single emission line) are the most reliable flags with a probability to be correct of 75%, 95%, 100%, and 80%, respectively.
A quality flag of 1 indicates a probability of being correct of 50%, while a quality flags of 0 indicates that no redshift could be assigned. The VVDS catalog has been matched to existing multiwavelength photometric catalogs. The catalogs we use in this paper contain the Canada France Hawaii Telescope Survey (CFHTLS; T0005 release) ugriz bands; these data were obtained with the Megacam camera. WIRCAM-JHKs data are also available from the WIRDS survey (WIRDS;  Bielby et al. 2012). We also used VUDS, a spectroscopic galaxy survey aimed at studying galaxy evolution in the redshift range 2 < z < 6+, that has targeted ∼10000 objects. Galaxies have been selected in three widely observed fields to mitigate cosmic variance: COSMOS, ECDFS, and VVDS-2h fields. For 90% of the survey, the selection was carried out with the photometric redshift method using the LePhare software  (Ilbert et al.
2006). For the remaining 10%, color-color Lyman break selection was used (ugr, gri, and riz diagrams). As for the VVDS, VUDS was carried out with the VIMOS spectrograph at the VLT and each target was observed for ∼14 h in the wavelength range 3500≤ λ ≤9350 Å at low resolution. Data were reduced with the VIPGI software and spectroscopic redshifts measured with the EZ tool. The reliability flag system is equivalent to that described above for the VVDS survey. The ECDFS field contains the U,B,V,R,I,Z,J,H,K bands and IRAC channels from the catalog assembled by  Cardamone et al. (2010)  in the MUSYC survey. The COSMOS fields come with u* band data from the CFHT, Subaru imaging (B, V, g+, r+, i+, z+) in the optical and Ultra Vista (J, Ks) ,and IRAC (two first channels) for the IR. Finally, for the VVDS-2h field observed in VUDS, we used u, g, r, i, z observations, which are available from the CFHTLS with Megacam down to i AB = 25.
Original sample Mg As we used spectroscopic data, the galaxy spectra we used must be corrected for any instrumental signature that may affect the measurements. As mentioned in Section 2, spectra have been processed following standard and rigorous methods. Nevertheless, some artifacts and residuals may still be present in the spectra. To tackle this problem we performed a spectral fit of our sample of 3711 galaxies via the new SPARTAN software (Thomas et al, in preparation, see Appendix A for a brief overview). The fit was performed considering a wide parameter space. We used physical conditions when building stellar population models. This parameter space is summarized in Table  2. We used BC03 models  (Bruzual & Charlot 2003)  with a Chabrier initial mass function  (hereafter IMF; Chabrier 2003).
1 in at least one of the two measurements. Based on the evolution given in Fig. 2 , this ensures to select galaxies with an age of at least ∼0. 5 Gyr. We then inspected all the candidates by eye and generate two subsamples. The first subsample is a secure catalog of galaxies with clear Mg UV index. This sample contains 75 galaxies. The mean redshift is 1. 22 with a dispersion of 0. 36. The mean Mg UV based on the measurement on data is 1. 46±0. 33, while taking the measurement from the best fit templates gives a mean Mg UV of 1. 41 with a dispersion of 0. 24. Examples of galaxies in the selected sample with their associated best fit from SPARTAN are presented in Fig. 3  and show that SPARTAN is able to reproduce very well the spectra of our objects. The redshift distribution of this most reliable sample is shown in Fig.
Joining the two samples together leads to a sample composed of 27 galaxies from the VVDS Deep survey (24 secure and 3 candidates), 37 galaxies from the VVDS Ultra-Deep sample (29 plus 8), and 39 objects from VUDS (22 plus 17). We compare the estimation of the Mg UV strength computed directly from the data and from the best spectral fit template. When computing the Mg UV on the data we estimate the error on the Mg UV index using the error propagation method. The results are presented in figure  5. In the secure sample, the median difference between the measurement in the best fit template and the data is 0. 05 while in the candidate sample the difference reaches 0. 22. This larger difference can be explained by the higher noise in the edge of the spectra. As noise can lead to large variation in the measurement  Best SPARTAN fit Fig.
Physical properties of our Mg UV -selected galaxy sample 4. 1. Stellar mass, star rormation rate, and dust extinction In this section we study the properties of our selected galaxy sample as function of the strength of the Mg UV index. As the spectra cover a limited rest-frame spectral window, we used the photometric data alone to perform this analysis. The physical parameters were then computed using SPARTAN capabilities of fitting photometric datasets. We used the photometric bands described in Sect. 2 to perform a SED-fitting analysis of our galaxies. The library of templates used for this purpose is built making use of low resolution BC03 simple stellar population models with a Chabrier (2003) IMF. The complete parameter space we used in this fitting is described in Table  3. From this fitting run we can look at the evolution of key quantities as a function of the Mg UV strength and we present these in Fig.
6. We stress that the evolution of these quantities is extracted from an independent analysis of photometric (for the physical parameters) and spectroscopic data (for the Mg UV index strength). It is also important to note that the binning is done on the physical parameters rather than on the Mg UV index itself, which explains why the absolute values of the index can differ from one plot to another. -Stellar mass and SFR: The relationship between the stellar mass with the Mg UV index strength indicates that the more massive the galaxies, the stronger the Mg UV index. This evolution goes from Mg UV ∼1. 2 for low-mass galaxies (M /M ∼9. 0) to Mg UV ∼1. 55 for high-mass galaxies (M /M ∼11). This confirms that galaxies exhibiting strong Mg UV index are in an already advanced stage of evolution with a large stellar mass.
Conclusions In this paper we studied a spectroscopic sample of galaxies selected via the strength of the Mg UV index. The galaxies come from three different surveys: VVDS-Deep, VVDS Ultra-Deep, and VUDS. -We measure the Mg UV index on 3711 galaxies in these spectroscopic surveys both from the data and from the best spectral fit and select 103 galaxies with Mg UV ≥ 1. 1. -We study the evolution of several galaxy physical parameters computed from an independent photometric fitting with respect to the strength of the magnesium index Mg UV : stellar mass, SFR, dust extinction, and age. We find a very strong correlation between each of these parameters and the specral index. We find that when its strength increases, the stellar mass of galaxies increases, their SFR decreases, the amount of dust decreases, and the galaxies becomes older.
Nevertheless, we give in Appendix C, the coordinates for each of these objects. We point out that 75% of the data we used in this paper are public and freely accessible 1 , 2. -The SPARTAN tool is available on GITHUB 3  and comes with all the inputs needed to make the code run. The version released at this moment allows for a separate fit on the photometry and spectroscopy, as used in this paper. The final version will be presented in a paper in preparation  (Thomas et al, in prep). We do not provide the full fit of the 3600 galaxies mainly for reasons of disk space, but the public version of SPARTAN comes with all the input to reproduce the results. -The IGM models used for this paper are publicly available in Zenodo  (Thomas 2019 ).
070. A-9007 and  177. A-0837. Based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA/DAPNIA,at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii. This work is based in part on data products produced at TERAPIX and the  Canadian Astronomy Data Centre  as part of the  CFHT Legacy Survey , a collaborative project of  NRC  and  CNRS.
5 M , and brown dwarfs orbiting Sgr A* are all detectable in one year of LISA data with a signal-to-noise ratio above 10 for at least 10 5 years in the slow inspiral towards either the innermost stable circular orbit (compact objects) or the Roche limit (main-sequence stars and brown dwarfs). The longest times in-band, of the order of 10 6 years, are achieved for primordial black holes of mass ∼ 10 -3 M down to 10 -5 M , depending on the spin of Sgr A*, as well as for brown dwarfs, just followed by white dwarfs and low mass main-sequence stars. The long time in-band of these objects makes Sgr A* a valuable target for LISA. We also consider bodies on close circular orbits around the massive black hole in the nucleus of the nearby galaxy M32 and find that, among them, compact objects and brown dwarfs stay for 10 3 to 10 4 years in LISA band with a one-year signal-to-noise ratio above ten.
Our implementation is based off the code developed for  Gralla et al. (2015). For the series method our code is based off of codes used in  Kavanagh et al. (2016) ;  Buss & Casals (2018). Both of these codes, as well as code to compute spin-weighted spheroidal harmonics, are now publicly available as part of the Black Hole Perturbation Toolkit 2. The final step is to compute the inhomogeneous radial solutions. In this work we consider circular, equatorial orbits. With a point particle source, this reduces the application of variation of parameters to junction conditions at the particle's radius  (Detweiler 1978). The asymptotic complex amplitudes, Z ∞ m , can then be computed by evaluating the radial solution in the limit r → ∞. The mode amplitudes are plotted in Fig. 2  as functions of the orbital radius r 0 for 2 5, 1 m and some selected values of the MBH spin parameter a.
where ρ ≡ 1. 41 × 10 3 kg • m -3 is the mean density of the Sun. The numerical resolution of Eq. (  44 ) for r R has been implemented in the kerrgeodesic_gw package (cf. Appendix A) and the results are shown in Fig. 13  and Table  2. The straight line behavior in the left part of Fig. 13  corresponds to the power law r R ∝ ρ -1/3 in the asymptotic formula (46). In Table  2 , the characteristics of the red dwarf star are taken from Fig. 1  of  Chabrier et al. (2007)  -it corresponds to a main-sequence star of spectral type M4V. The brown dwarf model of Table  2  is the model of minimal radius along the 5 Gyr isochrone in Fig. 1  of  Chabrier et al. (2009). This brown dwarf is close to the hydrogen burning limit and to the maximum mean mass density ρ among brown dwarfs and main-sequence stars.
2 M , same as in Table  2 ) and a main-sequence star of mass µ = 2. 4 M , which corresponds to a spectral type A0V. Let us mention that current observational data cannot rule out the presence of such a rather luminous star in the vicinity of Sgr A*: GRAVITY observations  (GRAVITY Collaboration et al. 2017)  have set the upper luminosity threshold to a B8V star, which is a main-sequence star of mass µ = 3. 8 M. T ins in-band appears to be very large, of the order of 10 5 yr, except for the 2. 4 M -star for the inclination angle θ = π/2, which has r R > r 0,max , i. e. it is not detectable by LISA. As already argued for compact objects, this large value of the time spent in LISA enhances the detection probability. Regarding main-sequence stars, we note that the recently claimed detection of a 149 min periodicity in the X-ray flares from Sgr A* (Leibowitz 2018) has been interpreted as being caused by a µ = 0.
Acknowledgements. We are grateful to  Antoine Petiteau  for having provided us with the LISA noise power spectral density curve and to  Pau Amaro-Seoane ,  Michał Bejger ,  Christopher Berry ,  Gilles Chabrier ,  Suzy Collin-Zahn , and  Thibaut Paumard  for fruitful discussions. NW gratefully acknowledges support from a  Royal Society -Science Foundation Ireland University Research Fellowship.
Highlights The conventional wisdom of the drugdevelopment roadmap strategy that seeks to establish therapeutic efficacy based upon knowing the molecular target and mechanism of action is being challenged. Cell-based phenotypic methods are allowing the high-fidelity host-cell/ pathogen niche to be used for highthroughput image-based cell analysis for drug screening. Phenotypic screening recapitulates biological relevance at the heart of drugscreening campaigns and is becoming a primary determinate to improved therapeutic outcome. Emerging methods using transcript quantification, public databases (chem/ bioactivity profiles, ontologies, imagebased screening results), combined with machine-learning tools, are providing ground-breaking new and alternative screening strategies by augmenting phenotypic screening results.
By contrast, HRMAn provides intuitive intelligent image analysis software capable of actually learning from the fluorescently labeled hostcell/pathogen images exactly how to distinguish and quantitatively assess host protein recruitment during infection. The open-source image analysis platform is based on machine-learning algorithms and deep learning, and is highly flexible, as evidenced by its capacity to learn phenotypes from the data without relying on researcher-based assumptions. Indeed, the system was shown to perform equally well on both parasite (T. gondii) and bacterial (Salmonella enterica Typhimurium) experimental models. HRMAn's capacity to recognize, classify, and quantify pathogen killing, replication, and cellular defense responses is likely just the first example of many to come using AI-driven approaches to enhance early drug discovery for infection.
An 842-dimensional quantitative vector readout calculated for each single cell was extracted from a three-channel microscopy-based screen performed for 524 371 chemical compounds measuring glucocorticoid receptor translocation. A bioactivity matrix was used to document the available experimental activities of the 524 371 imaged compounds in some 1200 assays that could be attributed to a specific protein target, and from a subset of 535 assays revealed by machine-learning, several dozen returned robust criteria for predictivity performance. These data were used to predict assay-specific biological activity in two ongoing drug-discovery projects. In these projects, repurposing increased hit rates by 50-to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits.