dataesr/scientific-paragraphs-categorization huggingface dataset:
1, 3, 4 Likewise, when results at high or low temperature were dominated by a single laboratory, the temperature range was restricted to exclude those results. 3, 4 he remaining weighted results were pooled, and either fitted directly to eq. 1 (using nonlinear least squares fitting), or transformed by taking the natural logarithm of kp and fitting a straight line as a function of 1/T. As the kp is determined by analysis of a MMD, usually obtained by SEC in which the elution volume of a polymer is approximately proportional to the logarithm of its molecular weight, errors in ln kp are constant, and this transformation of the data does not bias the parameter estimates. 2, 12 hile these strategies were quite effective at reducing the impact of a single laboratory on the combined datasets, they have some disadvantages.
The weighting procedure is somewhat arbitrary, while restricting the temperature range can involve discarding a significant quantity of data. The most important issue, however, is the assumption, implicit in the procedure of fitting a single line to the combined dataset, that the experimental errors in each point are uncorrelated. In practice, this is not the case. 8] [19]  As shown in Figure  1 , the resulting data set is dominated by the results of one study,  12  shown in grey. In an attempt to compensate for this, in the original analysis 1 the results of the other studies were given three times as much weight in the fitting. In Figure  2 , 95% JCRs are shown for the four studies  12, [17] [18] [19]  that provided sufficient data to estimate them, while the kp values at 25°C from the 4 remaining studies  [13] [14] [15] [16]  are represented as open squares, shown at an arbitrary EA.
A 95% JCR  30, 31  can be calculated for these parameters, but is significantly larger than the originally published JCR due to the severe reduction in degrees of freedom. The original dataset contains only 4 independent estimates of EA, compared to the 61 data points used to calculate the original JCR, while 12 estimates of EA are available using the expanded dataset. As a result, the critical value of the F-distribution used to obtain the JCR is taken from the F2,3 distribution (original dataset) or F2,11 distribution (expanded dataset) as 19. 1 or 8. 0, respectively, as opposed to the F2,59 distribution, which gives a critical value of 6. 4. § Figure  3a  shows the JCR for the expanded dataset (in blue), overlaid on the JCRs of the individual studies. This JCR encompasses significant portions of most of the individual JCRs and point estimates of kp.
Extension to all benchmarked monomers This approach to determining the JCR while taking into account interlaboratory variation works well when there are at least 5 independent studies. However, the majority of the IUPAC benchmark studies comprised 2 to 4 laboratories. With so few independent data points, the 95% JCRs become unfeasibly large. Thus, we sought an alternative method to estimate the typical interlaboratory variation in estimation of Arrhenius parameters for propagation rate coefficients by PLP. Assuming that the error was roughly constant regardless of the monomer being studied, we calculated the pooled interlaboratory covariance matrix of all 13 monomers for which Please do not adjust margins Please do not adjust margins benchmark Arrhenius parameters are available as the weighted average of the variances and covariances of the individual parameter estimates for each monomer, according to equation 7: 𝑠 𝑝𝑜𝑜𝑙𝑒𝑑 2 = ∑ (𝑁 𝑖 -1)𝑠 𝑖 2 𝑖 ∑ (𝑁 𝑖 -1) 𝑖 (7) The pooled variance included 81 independent estimates of ln kp and 71 independent estimates of EA.
Conclusions Systematic differences between laboratories have a significant effect on the results obtained from PLP studies of the temperature dependence of the propagation rate coefficient of monomers in radical polymerization. These differences occurred despite significant efforts to standardize experimental protocols and must be taken into account when analyzing the results of interlaboratory studies. Failure to do so leads to significant underestimation of the uncertainty associated with the Arrhenius parameters, and may produce erroneous estimates when the experimental dataset is dominated by the results of a single study. We have presented revised estimates of activation energies and pre-exponential factors (and their 95% JCRs) which explicitly account for the interlaboratory variation and also incorporate additional data sets published subsequently to the benchmark studies.
The data set from reference 4 (3 studies 55-57 ) was reanalysed. No additional data were found that complied with the IUPAC guidelines for determination of kp using PLP. Glycidyl methacrylate. The data set from reference 4 (2 studies 55,57 ) was augmented with an additional study 58 published in 2008. This study covered a temperature range of 60-195°C, but only results from 60-90°C were added to the dataset in order to avoid contamination of the kp data from depropagation. In this study, SEC with both light scattering detection and universal calibration were to obtain MMDs. The light scattering results were used in the current reanalysis. While the 3 studies gave consistent estimates of kp at 25°C, they differed quite significantly in their estimate of EA, ranging from 20. 3 to 26. 2 kJ•mol -1.
Matériels et méthodes La zone d'étude de la réparation écologique des écosystèmes corallien tropicaux se situe dans l'océan Indien, pris dans son acception la plus large, du canal du Mozambique à l'ouest, de la mer rouge au nord-ouest à l'Australie à l'est (Figure  1 ). Trois étapes ont été suivies pour la collecte et l'analyse des données : (i) la construction d'une base de données concernant les projets de réparation écologique en milieu corallien et les méthodes de restauration appliquées, (ii) l'identification des variables permettant de caractériser l'efficacité de la réparation et (iii) le traitement statistique du jeu de données (recoupement, croisement et classement relatif).
Conclusion Les travaux sur les facteurs de variation des teneurs en vitamines du lait et des produits laitiers publié par la communauté scientifique internationale sont assez épars dans le temps et entre pays. Ainsi, les premiers facteurs de variation liés à l'alimentation, ont été mis en évidence seulement au début des années 2000. Or, le tableau est loin d'être complet. Ceci a été pointé du doigt dans « conclusions» de l'expertise collective conduite par INRAE sur la qualité des produits animaux  (Prache et al. , 2022). Elles suggèrent ainsi que des études devraient être conduites et les bases de données Ciqual de la composition nutritionnelle des aliments pour l'alimentation humaine  (Anses, 2020)  devraient évoluer pour permettre d'intégrer la variabilité de composition des produits en fonction des conditions d'élevage, dont l'alimentation des animaux.
Cet article propose donc une approche exploratoire visant à étudier les discours des chercheurs des sciences sociales en Arctique. Il utilise des méthodes lexicographiques appliquées aux contenus des rencontres ICASS. L'ambition consiste à poser un premier jalon, à travers un décryptage des grandes thématiques SHS des études arctiques et de leur évolution. Ceci questionne les rapports de force inégaux entre disciplines comme entre institutions. Qui est représenté (ou pas/peu) ? Quels sont les réseaux de chercheurs visibles ? Un second objectif, plus méthodologique, s'attache à montrer les avantages et les inconvénients d'une approche métrique, assistée par ordinateur, conduite avec le logiciel ALCESTE. Autrement dit, les deux co-auteurs, géographe et linguiste, assument le caractère hybride de la problématique.
Dans un premier temps, les corpus, constitués des trois derniers ICASS  (2011, 2014 et 2017) , sont présentés dans leur diversité thématique, leur représentativité et leur évolution. Puis, les traitements à partir du logiciel ALCESTE sont développés, avec des résultats présentés, d'abord au niveau du discours pris dans son ensemble puis, à un niveau plus fin, à partir d'une sélection de mots spécifiques (indigenous, resource et urban). Ceux-ci ont été choisis pour leur aptitude à rendre compte de traits caractéristiques de la zone circumpolaire : présence de populations autochtones, espace de ressource, majorité d'urbains  (Canobbio, 2011 ; Larsen, Fondahl, 2014 ; Vaguet, 2016). À chaque fois, la méthode est appliquée aux trois dates afin de mener une approche temporelle. ressources naturelles arctiques, dans un contexte d'accroissement de la demande globale.
Toutefois, ces auteurs, qui ont utilisé la base de données ISI Web of Science, soulignent que celle-ci souffre d'une sous-représentation des sciences sociales, ellesmêmes déjà sous-représentées dans la recherche polaire  (Aksnes, Hessen, 2009). D'autres études, réalisées par l'Institut de l'information scientifique et technique (INIST-CNRS) confirment la faible représentation des disciplines de SHS, avec moins de 5 % des productions  (INIST, 2009 (INIST, , 2010)). Aussi, pour contribuer un peu plus à la compréhension de l'apport des SHS, nous avons pris le parti d'exploiter un matériau de première main pour l'étude du discours scientifique international actuel sur l'Arctique qui concerne spécifiquement les sciences sociales : les livres des résumés des communications à l'International Congress of Arctic Social Sciences (ICASS).
Une première approche permet de rendre compte de la diversité des pays d'origine des conférenciers et des terrains polaires étudiés. Sans surprise, les chercheurs qui contribuent le plus en termes de présentations sont issus de structures de recherches des pays riverains de l'Arctique (figure  1 ). En outre, ceux-ci sont les seuls à pouvoir accueillir l'événement et ce choix favorise les scientifiques du pays hôte. Ainsi, lorsque la Suède accueille le congrès, sa contribution passe de 4 % à 10 % et les pays voisins profitent de cette proximité ; la Finlande double sa représentation et la Russie en bénéficie aussi bien que dans une moindre mesure (figure  1     Le logiciel ALCESTE (Image, CNRS, 1986) permet une analyse quantitative de données textuelles assistées par ordinateur  (Matthes, Kohring, 2008 ; Tankard, 2001).
Le processus s'effectue en deux temps. Le premier appel concerne des propositions de session, et parmi celles qui sont acceptées, certaines sont fermées, dédiées à un groupe de travail. En effet, l'ICASS offre un espace autant qu'un temps de rencontre pour les projets en cours ou en incubation. Le second appel concerne les communications et les proposants doivent déposer leur résumé dans le cadre d'une session. Il est impossible, à ce stade, d'évaluer l'impact de ce fonctionnement sur l'élaboration de la base de données des résumés. En théorie, tout le monde peut proposer une session. Cependant, en pratique, les sessions relèvent le plus souvent de chercheurs de niveau international, qui disposent déjà d'un réseau. De même, pour les communications, leur sélection relève des responsables de la session.
Amanda Kernell 5 et Máret Ánne Sara 6 livrent d'excellents exemples de productions engagées, qui donnent à voir des perspectives de l'intérieur des communautés. L'art participe assurément aussi au processus de coconstruction des représentations sociales et pourrait constituer une piste à explorer. recursos", una flora y fauna polar bajo amenaza del cambio climático, etc. ). Estas representaciones son coconstruidas en un proceso donde los medios y científicos juegan un rol fundamental. Los resúmenes de las ponencias del Congreso Internacional de la Asociación de Ciencias Sociales del Ártico (ICASS) constituyen un importante y rico material que no ha sido objeto de estudio. Su exploración a partir de un análisis textual mediante el software ALCESTE, expresa de manera clara los temas con mayor relevancia para las ciencias humanas y sociales, las redes de investigadores en el Ártico y como la noción de calidad de vida y bienestar se transforman en un aspecto fundamental parte este territorio.
Sauver des vies humaines sur les littoraux par une meilleure connaissance scientifique Les recherches sont nombreuses dans le monde sur cette thématique des risques côtiers et les scientifiques français contribuent à cet effort collectif. Afin d'avoir une meilleure appréhension de ces apports scientifiques, une recension des thèses contribuant à la connaissance des risques littoraux a été réalisée. Nous avons retenu les thèses qui ont été soutenues dans les universités françaises de 2010 à 2020 en mobilisant les bases de données www. theses. fr et https://tel. archives-ouvertes. fr de même que des recherches sur les sites des différents laboratoires de recherche du territoire français. Ainsi, ce sont 76 thèses qui ont été soutenues en 11 années, soit presque 7 thèses en moyenne par an (Fig.
Materials and methods A brief description of the GR3D model and the key updates to improve and test the overall model robustness are provided in sections "GR3D model presentation and main improvements" and "Partial validation of estimated fish abundances". The new routine added to GR3D to quantify nutrient fluxes is presented in the section "Design of the nutrient routine". The updated code is available online at https://github. com/inrae/GR3D/tree/v3. 2. 1 and http://doi. org/10. 5281/zenodo. 4442030. For a more detailed description of the GR3D model, see Appendix A or  Rougier (2014)  and  Rougier et al. (2014).
GR3D model presentation and main improvements Population dynamics within the model GR3D is an individual-based stochastic model. It explicitly combines population dynamics with key life cycle events and climatic requirements. The GR3D model was written in Java using the "SimAqualife" software framework specifically designed for spatialized and individual-based simulations to explore the movements of aquatic species  (Dumoulin 2007). The model covers the entire anadromous life cycle and is divided into six sub-models depicting the key events, including (i) reproduction, (ii) growth, (iii) survival, (iv) downstream migration, (v) maturation, and (vi) dispersal and upstream migration (Fig. 1 ). A set of 42 parameters obtained from the primary literature, expert elicitation, or model calibration was used to define these different processes.
Environmental and biological data to run simulations Data regarding the distribution of allis shad used to run simulations were obtained from the EuroDiad 4. 0 database, which records the presences and absences of European diadromous species in a total of 350 river basins throughout North Africa, Europe, and the Middle East from 1750 to present day (https://data. inrae. fr/dataset. xhtml?persistentId=doi:10. 15454/IVVAIC). Based on this updated version of EuroDiad, 135 basins were integrated into the GR3D physical environment to cover the core range of the species. The major addition from  Rougier et al. (2014)  was the inclusion of the UK and Irish river basins. River basins were distributed along a latitudinal gradient from Morocco (Oum Er-Rbia (33. 3°N)) to northern Scandinavia (Vefsna (65.
8°N)), including the British Isles (Figure  A1 ). All of the 135 river basins were characterized by seasonal near-atmospheric surface temperatures at the outlet. Near atmospheric surface temperatures from 1901 to 2018 were extracted from the CRU database (https://crudata. uea. ac. uk/cru/ data/hrg/#info), which consists of an atmospheric interpolated gridded dataset from weather observations with a resolution of 0. 5°Â 0. 5°. Data were provided by the FIC (Fundación para la Investigación del Clima; https://www. ficlima. org/) as monthly means and stored as NCDF files. The 135 sea basins located in front of each river basin outlet were characterized by seasonal temperatures calculated as the mean between 12 °C (temperature of the Bay of Biscay sea bottom) and the seasonal temperatures in the associated river basin.
Partial validation of estimated fish abundances Nutrient estimates were a function of fish abundances, so it was relevant to test the robustness of GR3D estimates. To do so, model outputs were compared to observed abundances for a set of 13 well-studied river basins located in France, Portugal, and Spain, for which time series of annual abundances were available (Fig. A1 ). Observations of shad abundances were given for different time periods depending on data availability and monitoring status of the 13 rivers. For most rivers, observations ranged from the end of the 1990s to the early 2000s, with an exception for the Minho River benefiting from fisheries time series dating back to 1914. For each of the 13 rivers, annual abundances were averaged over the sampling period to be compared with abundances derived from our model from the same time range.
Data regarding allis shad spawner abundance in these rivers were either derived from fisheries landings, video counting systems at fishways or based on the number of spawning events recorded at the spawning grounds ("splash-based" method). All the data and methods used to estimate observed abundances were fully described in Appendix A. To assess the coherence between model estimates and observed values, we classified model estimates into four categories. Rivers for which estimates were lower than observations were in "Category 1". Rivers for which estimates were higher than observations pertained to categories from 2 to 4 depending on how far estimates were from observations. The category number increased with the magnitude of the difference (i. e. , less than two (Category 2), less than ten (Category 3), or more than ten times (Category 4)).
2), regardless of whether they were autochthonous or allochthonous: ð2Þ I n k;j ¼ Id n k;j þ Is n k;j The main sources of nutrient inputs in river basins were assumed to be carcass decomposition, gamete emission, and fish excretion. As there was limited field data available for allis shad, carcasses inputs were computed using the average mass of male and female fish that died before spawning (eq. 3). Gamete contribution was hence implicitly considered in the average total mass of the adults, which included both somatic and unspawned gonadic mass.
The relationships for spawned gonad mass were derived from the same geographical location after artificial reproduction at the Bruch experimental station. Data were obtained from the nonprofit association MIGADO (http://www. migado. fr/). The percent wet mass content of N and P for eggs and sperm were approximated using values for ovaries and testes taken from American shad, with h N g;m = 0. 0325 and h P g;m = 0. 00724 for males and h N g;f = 0. 03242 and h P g;f = 0. 0032 for females  (Haskell 2018 ) (Table  1 ).
9): ð9Þ I n j ¼ 1 31 Â X 1930 t¼1900 Id n j;auto t ð Þ þ Is n j;auto t ð Þ þ Id n j;allo t ð Þ þ Is n j;allo t ð Þ The nutrient inputs supported by allochthonous fish (i. e. , strayers) over the 135 river basins were represented with a Chord Diagram using the R package "Circlize" (Gu 2014; https://CRAN. R-project. org/ package=circlize) widely used in population dynamics and genetics to quantify human migrations or gene flows. River basins were displayed all around a circle and connected with links corresponding to nutrient subsidies related to allochthonous fish. An in-depth analysis of the nutrient dynamics at the river-basin scale was performed using the Garonne River as a case study because the Garonne River was the reference population for shad in Europe and, as such, was carefully studied by the scientific community.
Strengths and limits of the methodology By combining a nutrient routine with a mechanistic species distribution model, the capacity of shad to move N and P subsidies between marine and freshwater ecosystems across their distribution range was quantitatively assessed. This work constituted one of the first evaluations of the regulating services associated with a diadromous fish species across its distribution range. Compared to river basin-specific assessments, this study brought substantial insights into the interdependencies among river basins regarding nutrient supplies related to anadromous species. As our model was calibrated on historical presences and absences, abundance estimates provided by the model required validation using observed data (Table  2 ). These results suggested a somewhat limited agreement between model outputs and existing data, with estimated abundances much higher than observations in half of the 13 river basins used for validation (i.
The highest agreement was seen for intermediate categories 2 and 3. Data sources are indicated in the footnotes. a ICES (2015). b Mota (2014). c P. Jatteau (personal observations, 2015). d  Baglinière and Elie (2000). e Logrami (http://www. logrami. fr/). f EPTB Charente (https://www. fleuve-charente. net/). g Plagepomi (https://www. observatoire-poissons-migrateurs-bretagne. fr/). h GR3D estimates were provided for the 1996-2006 period, as there is no temperature available after 2010 in the CRU database. watershed. To be consistent, nutrient estimates must be displayed at a similar scale. Considering water surface only covers 1% of the whole Garonne River watershed (BD TOPAGE: https://bdtopage. eaufrance. fr/), the amount of N and P load by allis shad population would be over 31. 130 kg N•km -2 •year À1 and 5.
Description of the physical environment The physical environment of the model is composed of a set of both river and sea basins connected to each other and spatially geo-referenced. Both river and sea basins are characterized by seasonal temperature time series (T, °C), while river basins are also characterized by their surface (km 2 ). The physical environment included 135 river basins along the European Atlantic coast, ranging from the south of Portugal to the British Isles and Norway (Fig. A1 ). Description of the data used to compare model estimates and observations of allis shad abundances in the 13 wellstudied rivers Data regarding allis shad spawner abundance were derived from several sources. For the Lima, Minho, and Adour rivers, abundances were estimated from annual commercial fishery landings (e.
Acknowledgements This study was co-funded by  Agence de l'Eau Adour Garonne and Nouvelle Aquitaine Region  within the  SHAD'EAU/FAUNA  projects (coordination:  Françoise Daverat (INRAE) ) and the  Atlantic Area Interreg Project DiadES  (coordination:  Géraldine Lassalle  ( INRAE ) and  Patrick Lambert (INRAE) ). Many thanks go to the  Fundación para la Investigación del Clima  (FIC; https://www. ficlima. org/)for providing the CRU database temperature extraction. We are grateful to the French nonprofit association MIGADO (http://www. migado. fr/)for giving us access to shad biological characteristics at the Bruch experimental station. We also thank  Sébastien Boutry  ( INRAE ) for his sound advices in data visualization technics and the two anonymous reviewers for their constructive remarks on the manuscript.
Snow on the Ground Daily co-located automatic measurements of SWE and snow depth were obtained from the CanSWE database in Canada  (Vionnet, Mortimer, et al. , 2021)  and from the SNOTEL network in the US  (Serreze et al. , 1999). CanSWE includes automatic SWE and snow depth measurements from Alberta Environment and Park in Alberta and from the Ministry of Environment and its partners in BC. SWE is measured using snow pillows or snow scales whereas ultrasonic sensors are used to measure snow depth. Quality-Control (QC) procedures were applied to the snow data and include (a) range thresholding for SWE, snow depth and bulk snow density (define as the ratio between SWE and snow depth) and (b) automatic outliers detection using the robust Mahalanobis distance as in  Hill et al. (2019). More details about the QC are provided in  Vionnet, Mortimer, et al.