dataesr/scientific-paragraphs-categorization huggingface dataset:
However, it is worth noticing how the median population of the municipalities opening SPRAR centers in our data is 3612, suggesting that many hosting municipalities are tiny. The small size of these municipalities makes the magnitude of these effects more plausible. For example, the newspaper article by Corriere della Sera (2011) describes how, in 2011, in the municipality of Acquaformosa, which had approximately 1000 inhabitants, 13 migrant students represented 20 percent of the total student population. As observed for the population, SPRARs seem to have helped municipalities respond to a generalized decline in the number of students and keep an essential local public service as schools alive. In turn, this positive effect of SPRAR on compositional amenities can help explain how these reception centers could have made the municipalities that hosted them more attractive for natives, who were convinced to stay or to move from other municipalities.
These contrasting results call for more future research on the geographical redistribution of migrants also in contexts different from the Italian one. Finally, we develop the analysis using data aggregated at the municipal level. Future research could aim to collect data on the exact location of refugee centers within the municipal territory. This kind of data could allow the researchers to investigate further the mechanisms behind the impact of refugees' reception and on the attitudes of natives toward migrants and economic and social indicators, potentially shedding more light on spillover effects.
Sediment yield data Data on sediment volumes were collected from the monitoring of debris basin dredging at the outlet of 120 investigated catchments. Those structures are managed by the French torrent control service Office National des Forêts -Restauration des Terrains en Montagne (ONF-RTM; n = 44 debris basins) and by local stakeholders such as river managers or municipalities (n = 76 debris basins). Structures managed by ONF-RTM concern generally the most active catchments, which were acquired by the French government during the late 19th and early 20th centuries to perform reforestation and torrent control master plans  (Piton et al. , 2017). In these active catch- V 100 = 755. 1 • χ 1 • A 0,782 ? The equation is based on a sample of 16 events that occurred in the Uri region (Switzerland).
Depending on the activity of the catchment, dredging was carried out several times a year or as soon as the structure was partially filled. The dredging operations were mainly carried out using mechanical excavators and trucks. Estimates of volumes were generally made by counting the trucks evacuating the sediment and at lesser times by comparing topographic surveys. It is noteworthy that these measurements have uncertainties that are difficult to quantify but that we assume to be ± 25 % from expert knowledge. Due to the regular dredging of the debris basins, data could be collected continuously for each studied catchment; dredging operations covered periods ranging from 5 to 40 years (mean = 25 years). A total of 797 dredging operations were recorded (with a mean number per catchment of 7 and a range from 1 to 28).
In addition, another source of sediment supply data in the 120 basins comes from the national database on torrential events, BD-RTM (https://bdrtm. onf. fr, last access: 10 May 2023). Briefly, this database provides information on past events that triggered damages in the catchments (to protection structures, roads or buildings), giving details on any causes and eventually information on the process type (i. e. , debris flow or flood) and sometimes also the volumes of sed-iment transported. This database is complementary to the debris basin dredging data, as it provides data on sediment supply that occurred before the structures were built. Moreover, the event volumes are sometimes more reliable in cases where the sediment volume is higher than the capacity of the structure, since they include an estimation of all the deposits.
Estimation of sediment yield characteristics Average annual sediment yields were estimated by analyzing the sediment supply data within a temporal window cor- responding to the period of monitoring of the debris basin dredging (i. e. , from the year of the structure construction to the most recent year for which the managers provided dredging operation information). We estimated the mean annual production V m by taking into account the volumes of events and dredging volumes (data from the managers of the structure and the BD-RTM). The data check identified several suspicious structures with a clear change in dredging frequency, suggesting a change in the function of the structure (e. g. , when cleaning is abandoned, the structure tends to maintain a sediment regulation zone where flow spreads freely and pseudo-cycles of erosion and deposition occur freely).
Precipitation An analysis of the rainfall chronicles was carried out for each catchment in order to obtain rainfall quantile values. For this purpose, the rainfall data from the. The color of the dots and lines informs the intensity associated with the historical event ("Evt_int_1", "Evt_int_2", "Evt_int_3" and "Evt_int_unk" refer to minor, moderate, high and unknown intensity, respectively). The two vertical dashed lines in grey delineate the monitoring period. Panel (b) shows the adjustment performed from the observations to estimate the volumes. The monitoring period covers 35 years and presents 19 years with non-null observations. A GPD distribution was adjusted. COMEPHORE reanalysis were used (© Météo France; see https://radarsmf. aeris-data. fr/, last access: 10 May 2023). These data provide precipitation values at an hourly time interval, on a 1 km 2 resolution grid and over the period 1997-2017.
Sediment-contributing area: connected eroding areas We delineated the sediment-contributing area using the French GIS database of the forest inventory as a mask (BD Forêt ® V2, mapping vegetation units of surface > 5000 m 2 ; see https://geoservices. ign. fr/bdforet, last access: 10 May 2023). This database provides an accurate digitization of the different natural vegetation covers (with information about the vegetation type, e. g. , moorland, herbaceous formations, deciduous forest, coniferous forests). Land without vegetation cover can correspond to agricultural areas, artificial areas (e. g. , urban, road) or bare soil -i. e. , unvegetated soil, sediment or rock -often located in the headwaters. Here, areas with bare soil were considered potentially sedimentcontributing areas. The definition is thus not exactly the same as that used by  Haas et al.
Any surface area of connected bare soil or rock is however considered equally in our approach; their lithological differences are assessed in the geological index GI presented in the next sub-section. We then characterized if these areas were connected to the channel network (Fig. 5 ). For this purpose, following  Peteuil and Liébault (2011) , an area of bare soil was considered connected and thus part of the sediment-contributing area if (i) continuous bare soil was visible between the hillslope and the channel on the orthophotos or (ii) a permanent or ephemeral watercourse draining in this area was present in the BD TOPO ® database (base of detailed hiking maps usable down to a scale of 1 : 2000; see https://geoservices. ign. fr/bdtopo, last access: 10 May 2023). Bare-soil areas located upstream lakes or glaciers were considered disconnected.
Index of connectivity IC Calculation of the index of connectivity IC was implemented thanks to the SedInConnect stand-alone software  (Crema and Cavalli, 2018). It is based on a morphometric algorithm that computes the potential connectivity between hillslopes and a target area from a digital terrain model. Briefly, IC is defined as the logarithm of the ratio between an upslope and a downslope component expressing the potential for downward routing of the sediment-produced upslope and the sediment flux path length to the nearest sink along a flow line, respectively, for each grid cell of a catchment  (Borselli et al. , 2008; Cavalli et al. , 2013). It can be expressed as follows: IC i = log 10 W i • S i • √ A i i d i W i •S i , ( 1 ) where i is the index of the grid cell at which the computation is performed, A i is the upslope area (km 2 ) and d is the length of the steepest flow line between grid cell i and the target area (m).
RF models also produce measures of the im-portance of each predictor  (Liaw and Wiener, 2002; Morel et al. , 2020). Importance analysis helps to select explanatory variables for model formulation. We implemented several prediction models (both RF and LR) based on one or more explanatory variables. This modeling strategy aims to assess the improvement of sophisticated models (i. e. , multivariate random forest models) compared to parsimonious regressions. Statistical analyses were performed on R (R Core Team, 2020), and RFs were performed using the package RandomForest  (Liaw and Wiener, 2002). To assess the performance of the different models, we performed a leave-one-out (LOO) cross-validation procedure by leaving out each observation in turn, fitting a model with all remaining data, and then predicting the value of the left-out observation.
Conclusions Using a unique data set of sediment dredging in about 100 debris basins and associated historical information, we estimated the mean annual and event-driven sediment production of torrents located in the northern French Alps. Several geomorphic indicators of their catchments were extracted, including the catchment area, the sediment-contributing area and the Melton index, as well as several statistical values of the index of connectivity IC computed on these catchments, and other relief, lithological, and rainfall indexes. We used these geomorphic parameters to try to predict the sediment supply of these catchments using several statistical methods. Results showed that the ratio of connected eroding areas was the most important predictor of the sediment production volumes. In line with the previous works synthesized in Table  1 , this paper demonstrates that simple equations can predict sediment yield in torrent catchments with independent basin parameters that are simple and easy to determine.
Solid volume with return period of T years [m 3 ] Code and data availability. Many more details on this work are available in the research report of  Morel et al. (2022) , available here: https://hal. archives-ouvertes. fr/hal-03549827. The parameters describing each catchment (rainfall, morphology and sediment production) are available in a synthetic table in Table  S1 , and the time series of sediment production of each catchment and the associated extreme value fit are available in Fig. S5. Maps of every catchment and the raster grid of the weighting factor W usable to apply our method on the whole of the northern French Alps are available at the following repository: https://entrepot. recherche. data. gouv. fr/ dataverse/HydroDemo  (Piton and Morel, 2022). The R codes used to perform the analysis are available upon reasonable request by directly contacting the first or second author.
Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. The authors would like to thank the French torrent control service (ONF-RTM) and the many catchment stakeholders who provided dredging data on the torrents. Many thanks to Alexandre Mas for providing rain data COMEPHORE on the studied catchments. The authors also acknowledge useful and relevant comments by Lorenzo Marchi and an anonymous referee that greatly helped to improve the manuscript. Financial support. This study is part of the HYDRODEMO project, which is financed by the European Commission, European Regional Development Fund (FEDER-POIA program) and the Région Auvergne-Rhône-Alpes (FNADT-CIMA program). Review statement.
, 2020) , with estimates suggesting that nutrients sourced from the Southern Ocean contribute over 60 % of the nutrient pool in lowlatitude regions  (Fripiat et al. , 2021; Sarmiento et al. , 2004). Antarctic sea-ice dynamics also impact ocean mixing and circulation  (Abernathey et al. , 2016; Pellichero et al. , 2018; Pellichero et al. , 2017) , and biogeochemical exchanges with Antarctic sea ice can alter water column inventories of nutrients and subsequent export to more northerly latitudes  (Moore et al. , 2018). Despite the documented importance of Antarctic sea ice, few datasets are available to quantify associated biogeochemical processes and their influence on regional biogeochemistry, ecosystem functioning and ocean-climate feedbacks at the circumpolar scale  (Fripiat et al. , 2017; Lannuzel et al.
J o u r n a l P r e -p r o o f Macronutrient concentration data (nitrate, nitrite, ammonium, phosphate and silicic acid) from 245 ice cores taken from fast ice around Antarctica were compiled into an open-access database hosted by the Australian Antarctic Data Centre (URL: https://data. aad. gov. au/metadata/BEPSII_2023_Fast_Ice_Nutrients). Ice cores are summarised by their seasonal coverage in Table  1  and by sampling location with information on data contributors and relevant publications in the Supplementary Information (Table  S1 ). By Southern Ocean region, 21 cores were from the Indian Sector, 70 from the West Pacific Sector, 67 from the Ross Sea, 85 from the West Antarctic Peninsula (WAP), and 2 from the Weddell Gyre (Figure  1 ). Ice cores were collected between 1997 and 2021. Sampling effort was highest in November (n=93 cores), followed by December (n=37) and October (n=22), with fewer than 20 cores being available for all other months and fewer than 10 per month from February to June (Table  1 ).
Of these, 232 cores were from first-year ice, 12 cores were from multi-year ice and 1 was from second-year ice. For analyses of monthly median values for first-year ice, months with fewer than four cores were excluded. Data were contextualised using observations of sea-ice properties taken at the time of sampling, including ice thickness, texture, structure, age and salinity, snow thickness, and freeboard level. Ice thickness was generally determined from the length of ice cores extracted. On a small number of occasions, when full ice cores could not be extracted from thick multiyear ice, ice thickness was estimated using an EM Bird electromagnetic-induction system  (Sahashi et al. , 2022). Samples of under-ice water (n=120) and sub-ice platelet layer ice and interstitial water (n=111) were collected in some studies and are included where available.
Comparison of field and laboratory methods and data processing Sampling methods and laboratory analyses used in each area of interest were compared to exclude systematic biases in the analysis of the dataset. Most of the concentrations presented here were measured as bulk nutrient concentrations of melted ice core sections (n=1914). Cores were collected from fast ice using ice corers (0. 09 to 0. 14 m diameter) and cut into sections of lengths ranging from 0. 05 to 0. 75 m (mean 0. 15 m, median 0. 12 m). Sections were cut using clean stainless-steel saws and then melted in the dark at ambient temperature or ≤4°C. A small number of measurements are from sea-ice brine samples, obtained by centrifugation of ice core sections (lengths 0. 20 to 0. 75 m, mean 0. 34, median 0. 30, n=22;  Arrigo et al.
g. ,  Stief et al. , 2022). Nutrient concentrations were measured in filtered and unfiltered samples, either in laboratories on Antarctic research stations/ships shortly after melting or centrifugation, or in home laboratories following storage at ≤-18°C or by fixing with mercuric chloride and refrigeration at ≤4°C. No significant differences have been reported between samples for nitrate, nitrite or phosphate stored frozen or fixed and those analysed immediately  (Dore et al. , 1996; Kattner, 1999). Ammonium concentrations are subject to uncertainty associated with both freezing and fixing, although no significant changes during storage have been reported between filtered and unfiltered samples  (Dore et al. , 1996; Fawcett et al. , 2014; Kattner, 1999; Macdonald et al. , 1986). Underestimation of silicic acid concentrations driven by formation of secondary silicate precipitates during freezing was reduced as far as possible in this dataset by thawing samples over a prolonged period (>24 hours) or using a water bath at 50°C, in line with recommended procedures  (Becker et al.
, 2020; Dore et al. , 1996; Sakamoto et al. , 1990). The nutrient concentrations in this compilation are mostly from automated or manual colorimetric analysis (e. g. ,  Hydes et al. , 2010). Some studies analysed ammonium (NH 4 + ) concentrations using ortho-phthaldialdehyde (OPA) and fluorometry  (Holmes et al. , 1999). Specific analytical protocols varied across studies, but in general calibration standards were prepared using artificial seawater or low-nutrient seawater with salinities similar to the samples being analysed to avoid matrix effects. In this compilation, total soluble reactive phosphorus is referred to as phosphate (PO 4 3-) and total dissolved silicon is referred to as silicic acid (Si(OH) 4 ). Nitrite data are included where available (n=188). Because nitrite data are not available for over a fifth of cores (n=57), we use nitrate+nitrite (NO 3 -+NO 2 -) for all cores and refer to these concentrations as nitrate for simplicity.
This approach is reasonable given the small and relatively constant contribution of nitrite to the nitrate+nitrite pool (median contribution of 2% across the dataset). Data and supporting metadata were contributed from across the international scientific community working in Antarctic sea-ice biogeochemistry in. csv format and compiled into a master dataset. Further processing, manipulation and visualisation of the data was conducted in RStudio version 3. 5. 1 and Matlab R2018b. Nutrient concentrations are examined as a function of time, normalised ice core depth (% of ice thickness), and longitude to investigate the temporal and spatial variability of nutrient dynamics in fast ice and their exchange with surface waters. Nutrient concentrations are bulk measured values unless otherwise stated.
J o u r n a l P r e -p r o o f Monthly time series of air temperature (°C) at 10 m height, surface wind speed (m s -1 ), and total (diffuse + direct) surface downwelling photosynthetically-active radiation (PAR; W m -2 ), estimated by MERRA v2 models for 1980-2020  (Gelaro et al. , 2017) , were obtained from the Giovanni Online Data System developed and maintained by NASA GES DISC (URL: https://giovanni. gsfc. nasa. gov/giovanni/). These data were averaged over the circumpolar ocean region between the Antarctic coastline and latitude 64°S, as the region in which almost all fast ice occurs (Figure  1 ). A seasonal climatology of total fast-ice extent  (Fraser et al. , 2021)  is shown for comparison and to contextualise the observational data presented in this compilation. We also use chlorophyll a concentration data from the Antarctic fast-ice algal chlorophyll a dataset (AFIAC;  Meiners et al.
Seasonal variation in fast-ice physical properties: ice thickness and salinity Across the dataset, fast-ice thickness was minimal in early autumn as it began to form from freezing seawater (Figure  3a ). Thickness increased gradually over winter, reaching a maximum in November-December before decreasing rapidly as solar warming and strong winds caused ice melting, thinning, break-out and loss. Exceptions to this were in the multiyear fast ice at Syowa Station (Lützow-Holm Bay, East Antarctica), where ice thickness of over 4 m was observed in January and February (multi-year ice not shown in Figure  3a ). Multi-year ice was also observed at Drescher Inlet (Weddell Sea), with a thickness of 2. 2 m in January. The maximum thickness of first-year ice was up to 3. 3 m in the Ross Sea (indicating ice rafting), 1.
Median salinity values in both bottom and upper ice layers increased again from December to January. Seasonal profiles of ice-core salinity over the full ice thickness (Figure  4a ) showed a characteristic C-shape in autumn and winter, albeit with some small-scale variability. C-shaped profiles became more pronounced overall through spring and into summer when they were most pronounced, with higher values in upper and bottom ice and lower values in the interior ice layers, and the lowest median bulk salinities were observed. Sea-ice salinity was substantially lower than the mean of seawater values (34. 3) throughout the sea-ice cycle, and we observed no clear spatial patterns in sea-ice salinity across the dataset.
Observed variation in fast-ice macronutrient concentrations 3. 3. 1 First-year ice Figure  3  shows the seasonal variation (monthly median values and interquartile ranges) in measured nutrient concentrations in the upper, interior and bottom ice layers of first-year Antarctic fast ice over the annual cycle. Chlorophyll a concentration data from the AFIAC dataset  (Meiners et al. , 2018)  are also shown for each ice layer for comparison to the nutrient data. Figure  4  shows the vertical distributions of salinity and nutrient concentrations by season sampled. Multi-year sea ice sampled in January and February showed differences compared to first-year ice, so these data are plotted and analysed separately (Figure  5 ). Fastice nutrient concentrations are compared to concentrations measured in ice-covered or adjacent surface waters, shown in Figure  6  and presented in Section 3.
4 ± 2. 1 µmol L -1 ; Figure  6d ), silicic acid was drawn down over the full ice thickness through spring to minimum values in summer (<15 µmol L -1 ). Silicic acid maxima were observed in bottom ice layers in a small number of cores through October to December, with some correspondence with the high nitrate concentrations in these layers. Our dataset showed no clear spatial patterns in silicic acid concentrations around Antarctica.
Nutrient concentrations in surface waters Surface water (≤20 m) nutrient concentrations from a coastal site in Marguerite Bay over a three-year period (2013 to 2016;  Henley et al. , 2020)  are used to represent the seasonal cycle in locations influenced by fast ice around Antarctica (Figure  6 ). These year-round data are compared to seasonal data from the eastern Ross Sea in November 1997 and 1999 and November and December 2004  (Cozzi, 2014). Seasonal minimum and maximum values for Marguerite Bay are shown in Table  3  and monthly values for the eastern Ross Sea are shown in Table  4. Table  S2  summarises water column (0-800 m) nutrient concentrations from studies associated with fast ice around Antarctica. Similarity of values and trends across these datasets shows that seasonal variability is substantially larger than spatial variability around Antarctica, such that the Marguerite Bay data presented here are broadly representative of these shelf environments for the purposes of comparing this variability to that in fast ice.
Total ice algal biomass has been shown to be greater in fast ice than in pack ice around Antarctica, and this has been hypothesised to be a consequence of higher availability of both light and nutrients to fast-ice communities  (Meiners et al. , 2018). Light and nutrient availability are both influenced strongly by the thickness and structure of the ice and overlying snow cover, as well as ice temperature and salinity and their combined effect on ice porosity and permeability  (Castellani et al. , 2020; Cozzi, 2008). Comparing our dataset with a compilation of Antarctic pack-ice nutrient data  (Fripiat et al. , 2017)  shows that the concentrations of nitrate, ammonium and silicic acid are higher and more variable in fast ice than in pack ice around Antarctica, especially in bottom ice layers (Figure  7 ).