dataesr/scientific-paragraphs-categorization huggingface dataset:
For R 2 D 2 outputs, the RMSE value is quite large (RMSE=1. 51), suggesting a strong modification of temporal properties. It can be linked with the underperformance of R 2 D 2 already observed in Fig. 11m  for the correction of LR SAFRAN. Concerning dOTC outputs, the RMSE value (= 0. 42) is slightly higher than those observed for LR SAFRAN and QQ outputs. It suggests that the influence of the correction of univariate distributions and spatial dependence on temporal properties The MOS approach that considers biases to refer to systematic distributional differences between references and simulated climate variables was found to be more appropriate for the implementation of the MBC-CycleGAN method and was chosen to be applied for the rest of the study. The second cross-validation method, that defines chronologically calibration and projection periods, was then used to evaluate the ability of the MBC-CycleGAN method to adjust climate datasets in a nonstationary context.
As IPSL simulations and SAFRAN references present different marginal and spatial changes between calibration and projection periods, two additional climate datasets (LR SAFRAN and IPSLbis) with changes that are in line with the references were specifically constructed and adjusted, allowing to better assess the quality of the corrections provided by the new method depending on the statistical biases of the data to be corrected. A wide range of metrics has been used to evaluate bias adjustment outputs with references and initial climate data and assess the corrections of univariate distributions, spatial correlations and temporal properties. In addition to the 1d-QQ method, two state-of-the-art MBC ( R 2 D 2 and dOTC) methods have been implemented and used as benchmarks to better evaluate the influence of nonstationary properties on the results of the MBC-CycleGAN method.
The results indicate that all the (M)BC methods implemented in this study generally present similar corrections of univariate distributions. Regarding spatial properties, the benefits of using MBC methods are clear compared to the 1d-QQ method. The MBC-Cycle-GAN method produced reasonable adjustments of spatial correlations with respect to R 2 D 2 and dOTC methods for both temperature and precipitation and the three different climate datasets to adjust. Concerning the temporal aspect, the MBC-CycleGAN method is not designed to correct this specific statistical property and tends to conserve the temporal sequencing of the time series to correct. Combined with the corrections of spatial features, this property has proved to be particularly interesting for the applications of MBC-CycleGAN when the data to correct temporally match the references (e.
g. , as for LR SAFRAN and SAFRAN dataset, see Sect. 5. 2. 2). The proposed method indeed outperformed all the others (M)BC alternatives for the correction of LR SAFRAN by generally presenting both spatial and temporal statistics closer to those from the references. Concerning nonstationary properties, it has been found that changes of both marginal and spatial properties between the calibration and projection periods of the climate data to adjust can have a non-negligible effect on the quality of corrections from the MBC-CycleGAN algorithm, and more generally from all (M)BC outputs. In a general way, better results are obtained for the corrections of simulations with changes that are in agreement with those from the references, whether the MBCs make the assumption of nonstationarity of marginal properties and dependence structures or not.
g. , CDF-t or QDM,  Vrac et al. 2012; Cannon et al. 2015 ) can of course be employed instead of QQ, as long as they do not modify (too much) rank sequence of temperature and precipitation time series and thus do not distort the convergence of the CycleGAN procedure. Concerning changes of spatial properties, the CycleGAN approach as implemented in this study is based on the key assumption that the conditional distributions | and | are the same in the training (i. e. , calibration) and test (i. e. , projection) datasets. It results in our context in making a strong assumption on copula stationarity between present and future periods. Although spatial dependence structures can be considered to be stable in time as imposed by physical laws over a specific region of interest (e. g. , Vrac 2018), it can not be generalized to each of the physical variables and regions.
For example, more concentrated spatial rainfall events are expected with higher temperatures in the future  (Guinard et al. 2015; Wasko et al. 2016). Therefore, should the changes in spatial properties in the simulations between calibration and projection periods be reproduced in the correction? By comparing our results obtained with different levels of nonstationarity in the model evolution and with two well-established MBCs based on copula stationarity ( R 2 D 2 ) and nonstationarity (dOTC) for future periods, we shed light on how the nonstationary properties of the simulations are taken into account by the different multivariate BC methods. The benefits of considering MBC methods assuming copula nonstationarity for the correction of such climate dataset are not always as clear-cut as expected compared to MBC methods assuming copula stationarity.
2014). Although producing reasonable results of adjustments for temperature and precipitation spatial distributions of IPSL and IPSLbis datasets, the outperformance of MBC-CycleGAN observed for the correction of LR SAFRAN is not obtained for these climate outputs. A possible reason explaining why the performances of MBC-CycleGAN differ between these three exercises of correction concerns the importance of the distributional differences between the inputs and target dataset considered. Indeed, unsupervised image-to-image translation algorithms such as CycleGAN can present difficulties to map two random variables and with probability distributions that exhibit strong differences  (Gokaslan et al. 2019; Royer et al. 2020). As LR SAFRAN presents smaller bias with the references than IPSL and IPSLbis data, outstanding results are obtained for the correction of LR SAFRAN with MBC-CycleGAN, while more moderate quality results are produced for IPSL and IPSLbis.
Availability of data and material The IPSL-CM5A-MR model data simulations as part of the CMIP5 climate model simulations can be downloaded through the Earth System Grid Federation portals. Instructions to access the data are available here: https://pcmdi. llnl. gov/mips/ cmip5/data-access-getting-started. html, last access: 06 September 2020, (PCMDI, 1989). The SAFRAN reanalysis dataset is available upon request to the French National Centre for Meteorological Research (CNRM, Météo-France CNRS).
Individuals tracked with light-level geolocators arrived well before the snowmelt in 2020 and clutch initiation dates across the population were earlier in 2020 compared to 2019. However, observations from a citizen science database and nest monitoring data showed that the arrival-breeding interval was shorter in 2020, thus the advance in timing lagged behind the environmental conditions. While hatching success was similar in both years, fledging success was significantly reduced in 2020. A trophic mismatch in early 2020 could be a possible explanation for the reduced reproductive success, but alternative explanations cannot be excluded. Our results show that, despite the timely arrival at the breeding grounds and a contraction of the arrival-breeding interval, Wheatears were not able to advance breeding activities in synchrony with environmental conditions in 2020.
We thus provide an important baseline for understanding migration and how this relates to breeding season ecology, in our population of wheatears. We used light-level geolocators to track wheatears across their annual migration and thus define their arrival at the breeding grounds, and collected detailed information on individual breeding activity and success as well as environmental conditions during the reproductive season. In addition, we used citizen science data on wheatear observations in order to quantify estimated arrival and transition dates of wheatears in the wider area of our study site, and potential longer term trends. Environmental conditions were measured both in the field and using remote sensed images in order to quantify seasonal vegetation development (i. e. , spring green-up, the seasonal development of new vegetation growth).
Environmental Conditions To investigate the longer-term trends in the annual date of snowmelt ("the snow free date"), we used the remotely sensed MODIS (MOD10A1. 006) Terra Snow Cover Daily Global 500 m dataset with recordings starting in 2000  (Hall et al. , 2016). Daily values for all pixels (N = 68) within the study area were extracted from Google Earth Engine  (Gorelick et al. , 2017)  via the R package rgee  (Aybar, 2020). For each pixel and each year, we identified the snow free date as the first date without snow that was followed by at least 60 days (approximately 2 months) of snow free conditions, ignoring potential snow free periods within the winter. To derive higher-resolution snow free dates for the actual nest sites in the study area during 2019 and 2020, we used the remotely sensed Sentinel-2 MSI MultiSpectral Instrument Level-2A dataset with a spatial resolution of 10 m  (Drusch et al.
, 2012). On the Google Earth Engine Server, we first selected all images taken in 2019 and 2020 intersecting at least one of the nest sites. Next, we masked cloud pixels using the Sentinel-2 Cloud Probability layer with the standard probability threshold of 60. We then calculated the normalised differenced snow index (NDSI, Band 8 and 4) and the normalised vegetation index (NDVI, Band 3 and 11). For each nest site and each image, we extracted the mean NDSI and NDVI values of all pixels within a 10 m radius (N = 2-4) around the nest. For each nest site and for both years, we fitted a smooth line (loess fit with span = 0. 2 from the R package stats) to the NDVI and NDSI values over the day of the year, and extracted the day when the model fit fell below 0. 1 NDSI (less than 10% of snow cover was defined as the snow free date) and exceeded 80% of the NDVI annual amplitude (the defined threshold when the peak in spring green-up was reached).
In the field, snow cover and grass height, as proxies for the spring green-up were monitored throughout the two breeding seasons (see Supplementary Table  1  for details and sample sizes). Data were collected using a standardised protocol for territory mapping. In summary, we defined four sectors above the treeline (total area: 161 ha) that were characterised by open grassland habitat, variation in elevation (∼ 700 m) and a high density of wheatear territories. We defined a route (∼ 6 km in each sector) along which we defined habitat survey points separated by a distance of 200 m (6-8 points each sector, 30 in total). Territory mapping and habitat point surveys were conducted over five visits between 27th May and 21st July 2019. Positions of singing males were recorded with a GPS device during the period between sunrise and 12 am and during suitable weather conditions.
As a control for the effect of geolocator devices on return rates, an additional 16 individuals were ringed, but were not fitted with a geolocator. All animal handling and protocols were carried out in accordance with relevant guidelines and regulations under licences issued by Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA, protocol nos. 27303 and 2463) and Città di Torino  (licence nos. 56. 1433/2019 and 197. 4765/2019). Light-intensity data were recorded at 5 min intervals and analysed using a threshold method  (Lisovski et al. , 2020a). Sunrise and sunset events (twilight events) were identified on logtransformed light data and a threshold of 1 log lux, using the R package TwGeos  (Lisovski et al. , 2015). Twilight events recorded at the known deployment site during periods after deployment and before recapture were used for calibration, i.
e. , to estimate the error distribution of twilight events and the individual reference sun elevation angle (position of the sun when twilight events were detected). For recordings from Migrate Technology Ltd. , tags, migratory movements were identified as sudden and directed changes of consecutive sunrise or sunset events (for details, see the "invMovement" method description in  Lisovski et al. , 2020b). Stationary periods with a minimum of two consecutive twilight events were then identified as periods surrounded by migratory movements. For data recordings from Swiss Ornithological Institute tags, we used the additional air pressure measurements to identify movement periods with a change in a pressure threshold of 4 (change in the altitudinal position of the bird), using the method implemented and described in the R package PAMLr (Dhanjal-Adams, 2019).
A symmetrical Gaussian curve was then fitted to the probabilities over time, and departure and arrival was calculated as the date when the curve fell, respectively, below or exceeded a probability of 0. 5 (as well as the 0. 025 and 0. 975 probability to report the 95% confidence intervals). To estimate the general migration transition of wheatears migrating northward from African wintering ranges into the regions of Piedmont and Aosta Valley, and thus elaborate a proxy for the arrival in the wider area of the breeding site in the Western Alps in 2019 and 2020, we additionally used citizen science data of wheatear sightings during spring submitted via the ornitho. it platform. The data includes the number of all observations of wheatears per day in the period March-May. We furthermore used data from the years 2010 to 2020 to describe changes over a longer period, being aware of the bias in the data that might include not only local breeding wheatears, but also individuals migrating further north.
Thus, we included all nests monitored in our analyses, except for the nests known to be replacements. We applied a general linear mixed model with binomial errors to quantify the success rate of hatching and fledging (a, b) of each egg laid within the population, with the fixed effects "year" (2019, 2020) and "period" (hatching, fledging), adding "nest id" as a random effect [using the function glmer from package lme4  (Bates et al. , 2007) ]. To quantify the nest success rate (c), we applied a generalised linear model with the fixed effect "year" (using the function glm from package stats). Model assumptions were tested by deriving residual plots and checking for overdispersion [function dispersion_glmer from package blmeco  (Korner-Nievergelt et al. , 2019) ]. Conclusions from test statistics were based on the credibility intervals from the model predictions calculated using the function sim from package arm  (Gelman and Hill, 2007) , and by drawing 2,000 random samples from the posterior distribution of the model parameters to obtain their 95% credible intervals from these simulations.
Migration Schedule and Arrival Dates In 2020, we retrieved 11 geolocators, of which nine contained sufficient data for further analyses (6 Migrate Technology, 3 SOI, of five males and four females). Fourteen tagged individuals were resighted (n = 35, return rate: 40%) of which 13 were recaptured. Two individuals lost the device. Nine adults of the control group were resighted (n = 16, return rate: 56%). The frequency of return rate between tagged and control groups was not significant (Fisher exact test, P = 0. 126). Six tags recorded light levels for the full annual cycle. One tag stopped recording after arrival at the main wintering site (24EA). Two tags stopped recording during spring migration (24DK, 24EP). In total, we derived arrival dates at the breeding site from six tracked individuals in 2020 (Figure  2B  and Table  1 ; see for details Supplementary Figures  1A-C  and Supplementary Table  5 ).
However, individuals migrating through the area can come from various wintering sites in Africa and can vary even more strongly in their breeding destinations. Population-specific migration schedules, such as the later transition of northerly populations  (Maggini et al. , 2013) , and potential trends may thus be hidden in the sightings dataset. In fact, wheatears from Greenland (Oenanthe oenanthe leucorhoa) have advanced their migration within the last three decades  (Desaever and Bairlein, 2020)  and previous studies on a northern lowland population of the wheatear have shown a continuous trend toward earlier arrival at breeding sites  (Low et al. , 2019). Constant effort trapping data of wheatears stoppingover on islands in the Mediterranean Sea has revealed a weak, but not significant, trend toward earlier passage during spring migration within the last 18 years  (Maggini et al.
, 2020). The tracked individuals in this study provided exact arrival dates for 2020, showing that the studied population might arrive earlier than the major migration flow of wheatears in the region (Figure  2B ), including birds of northerly populations  (Maggini et al. , 2013). This might be also true for 2019. However, citizen science data enabled us to compare the overall timing of arrival and transition of wheatears in the Western Alps for both years, showing that birds were recorded about 10 days later in 2020, which implies a further shortening of the arrival-breeding interval in 2020. There are known biases associated with citizen science data (e. g. ,  Callaghan et al. , 2017) , including variations in spatial and temporal survey effort. In 2020, restrictions on people's movement were imposed in March in Italy as a measure against the COVID-19 pandemic.
ACKNOWLEDGMENTS Thanks are due to  Chris Fiedler ,  Ursula Scuderi , and  Liam Kelly  who contributed to the environmental data collection, nest monitoring and ringing, including geolocator deployment and retrieval. We are also grateful to  Heiko Schmaljohann  for giving specific advice for fieldwork methods and trapping, to  Michele Ottino  and staff of the  Ente di Gestione delle Aree Protette delle Alpi Cozie  for logistical support, and  Enrico Caprio  and ornitho. itfor providing the citizen science data. This work is a collaboration between the  University of Turin  and the  Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research in Potsdam , which was the hosting institute during data analysis and manuscript writing.
FUNDING This project was funded by the  Ph. D. scholarship  ( University of Turin ) of MS. The  German Ornithologists' Society  supported this project with a research grant (designated to MS) to fund the  Migrate Technology Ltd. , geolocators. SL acknowledges funding by the  Geo. X Research Network for Geosciences in Berlin and Potsdam. We acknowledge support by the  Open Access Publication Funds of Alfred Wegener Institute Helmholtz Center for Polar and Marine Research.
DATA AVAILABILITY STATEMENT Geolocator data including the raw light recordings, the annotated twilight files, the location estimates with credibility intervals, and the R code will be uploaded onto Movebank (www. movebank. org) upon paper acceptance. R code for Satellite data manipulation and Northern wheatear sightings can be accessed at https://github. com/slisovski/Sander_et_al_Analyses and will be uploaded to a public repository (Zotero) upon paper acceptance. Field observations including nest site occupation, clutch size, hatching, and fledging success as well as snow and vegetation monitoring data are available upon request.
The ability of these models to reproduce and predict the response to treatments will allow them to contribute to new therapies. In addition to novel experimental approaches described above, computational systems biology tools will allow for additional avenues for exploration at multiple biological scales. This will enable more accurate comparison between in vitro and in vivo and more detailed integration of multi-parameter datasets and further improvement of the experimental design. Large scale studies which employee these models along with detailed pathological analysis, for example combination of histomorphology, molecular, genetic, and metabolic characterization, will be required to develop a full, scalable, sensitive, and reproducible in vitro model that could be robustly employed in the evaluation of compound efficacy and safety.
, 2006) : NBL and TV calculated are shown in Table  4 , resulting from the application of the selected methodology to the dataset. The values for NBL are presented as minimum and maximum. The TV was calculated by using the maximum NBL values. Table  4  shows that the concentrations in chemical elements are mostly above the NBL in shallower water from hand-dug wells and springs than in deep water from boreholes. This can be explained by the fact that shallow groundwater is more sensible to anthropogenic influences. However, samples presenting HCO 3 -contents above the NBL are mostly observed in boreholes. These samples correspond to the deepest groundwater. Since, this element is only related to natural processes it is therefore clear that the deeper the groundwater flows, the more the groundwater mineralization increases due to water-rocks interactions.
Dimensions and variables Following the methodological approach described in Section 2, Section 4 defines the relevant variables based on our methodological approach: literature review and the Normandy region's energy transition policies. It also justifies the methodological choices of normalization, weighting and aggregation of the index, which are mandatory steps in the construction of a synthetic index. The construction of the NETSDI is based on six main fields defined from the regional strategy for energy transition and sustainable development. The six domains are then subdivided into sub-components and then into indicators (see Table  1  in the annex). The six components of the index are illustrated in Figure  1 , along with the sub-components and associated indicators. Indicators are selected following two main criteria: data availability and the ability of the selected indicator to monitor the efforts of local policies.
Environmental in situ Observation Data Environmental data were collected from long-term in situ monitoring stations (see Figure  1  for the location of sampling sites and Table  1  for their characteristics). COAST-HF-Iroise (Rimmelin-Maury et al. , 2020) and COAST-HF-Smart  (Petton et al. , 2020a)  are automated moored buoys that measure a wide range of environmental parameters (temperature, salinity, turbidity, dissolved oxygen concentration, fluorescence, and pCO 2 /pH). Salinity is derived from a conductivity sensor (accuracy ± 0. 03 psu). Measurements are taken at 2 m depth for the COAST-HF-Iroise buoy and at 50 cm over the bottom sediment for the COAST-HF-Smart buoy. The COAST-HF-Iroise buoy is located at the interface between the Bay of Brest and the Atlantic Ocean (Iroise Sea) where fresh and marine waters mix.
The depth of the mooring site ranges from 5 to 12 m, depending on the tide  (Blain et al. , 2004). The COAST-HF-Smart buoy is located in the eastern part of the Bay of Brest to monitor the land-sea continuum. The mooring site is located in a shallow embayment with a maximum depth of 8 m and receives the freshwater inputs from the Mignonne River  (Petton et al. , 2020a). December to March data were analysed from 2000 to 2018. All data are available on https://doi. org/10. 17882/74004 (Rimmelin-Maury et al. , 2020)  or www. coriolis-cotier. org/ and the two high-frequency 1  monitoring buoys are part of the French COAST-HF national observation network 2   (Cocquempot et al. , 2019; Farcy et al. , 2019; Répécaud et al. , 2019). To assess the impact of freshwater inputs on salinity changes, we used daily flows of the three main rivers, measured daily by the regional water authority Agence de l'eau Loire-Bretagne 3.