dataesr/scientific-paragraphs-categorization huggingface dataset:
As our simulation system is only able to represent snow in open areas, the forests, glaciers, lakes, and rivers in the study zone are masked in our simulations and observation datasets. For the forest mask, the BD FORET® V2 dataset has been used (IGN©, 2021a) with a masking threshold of 25% of forested sub-pixel area. Waterways are masked following data from BD TOPO® (IGN©, 2021b). In the valley, the urban areas around the city of Bourg d'Oisans, Allemont, and Saint Michel de Maurienne are also discarded from the analysis.
, 2016; Vionnet et al. , 2019; Deschamps-Berger et al. , 2022). Therefore, other precipitation estimates are also considered in this study, respectively from AROME NWP system and ANTILOPE radar-based analysis. An illustration of the mean daily SAFRAN precipitation spatial variability can be seen in Appendix Fig. B1 (a ). This map informs us of the spatial and inter-model variability and of the absolute precipitation values of each precipitation dataset.
Model experiments To disentangle the impacts of the simulation of blowing snow from the precipitation forcing, 250 meter resolution simulations and the flux limiter is activated  (Guyomarc'h and Mérindol, 1998; Baron et al. , 2023). In addition, blowing snow sublimation is activated using SBSM-like equations  (Essery et al. , 1999; Baron et al. , 2023). To investigate the role of unresolved transport variability of snow transport, 2 additional simulations were done at 30 m resolution with and without the SnowPappus transport module (see HR standing for High Resolution). Due to the higher numerical cost of these simulations and the increased complexity of downscaling and validating weather forcing at this resolution, 30 m resolution simulations were only run with the SAFRAN precipitation fields.
The Pleiades stereo-imagery In order to evaluate more directly the spatial variability of the simulated snow height, we used snow height maps derived from Pleiades stereo-images  (Deschamps-Berger et al. , 2020; Marti et al. , 2016). In both cases, snow height is defined following The forest, glaciers, and lakes on the domain are masked both for the Pleiades observations and simulations using the mask defined in Sect. 2. 1. An additional mask of the No Data pixels in the Pleiades data is applied to the simulations for accurate comparison.
Across all elevation ranges, the mean driver of simulation bias appears to be the precipitation dataset choice. The addition of snow transport increases and improves the simulated standard deviation, mainly above 2500 m and increasing with elevation. For these upper elevation bands, the snow transport prevails in the choice of the precipitation dataset on 370 this criteria. For the lower elevation bands, the main driver of simulation standard deviation appears to be the choice of the precipitation dataset. The simulations using AROME precipitation have the biggest standard deviation value of all simulations.
(2021). In a simulation with a precipitation dataset leading to an already negatively biased snow height (ANTILOPE), the addition of snow transport further degrades the overall snow height simulation bias. Similar behavior is found on the SMOD in Fig. 10  (a) and (d) with an exception for the 30m simulation where it increases the absolute SMOD bias. Thus the beneficial or detrimental impact of blowing snow transport on mean snow height or SMOD bias is mainly dependent on the precipitation bias. This result is consistent with  Raleigh et al. (2015) ;  Schlögl et al. (2016) ;  Günther et al. (2019) , also finding the prevalence of meteorological forcing errors in snow simulations.
On the precipitation forcings As the Crocus snow model is known to produce realistic snow heights and snow water equivalent when forced by wellcontrolled meteorological forcing  (Lafaysse et al. , 2017; Menard et al. , 2021) , the large biases of snow height and SMOD obtained in the simulations presented in this paper probably largely reflect intrinsic biases of the different precipitation dataset. More specifically, when looking at the simulation forcing with the ANTILOPE precipitation source, we found that across all elevation bands, the ANTILOPE forcings have the lowest snow height and SMOD values. Furthermore, a negative bias trend is seen with elevation. The ANTILOPE product is primarily based on radar reflectivity measurements, known to be more challenging in mountainous terrains  (Yu et al.
Soil properties are also an important parameter influencing snowpack simulations but they are often overlooked. Our simulations use the 1km resolution ECOCLIMAP landcover database  (Champeaux et al. , 2005)  and the 1km Harmonized World Soil Database (HWSD) v1. 10  (Nachtergaele et al. , 2009)  as the reference for soil properties. The spatial variability of soil properties in mountains is significant and the 1km resolution of the HWSD database might not capture enough of the spatial variability, leading to spatial errors.
Impact of the grid cells classification in spatial evaluations Classically in the snow modeling community, the quantitative spatial snow evaluations are performed by grouping simulation points  (Grünewald et al. , 2014; Vionnet et al. , 2022; Monteiro et al. , 2022; Tong et al. , 2009; Deschamps-Berger et al. , 2022; Vionnet et al. , 2017)  because (1) it allows to summarize large simulations dataset with more concise diagnostics and (2) these evaluations are less demanding for models than pixel-to-pixel evaluations. In Sect. 3. 3, results are presented using landform classification instead of elevation bands. The results are partly correlated because summits and ridges mainly cover the highest elevations of the domain (Fig. 3 ). Thus, we consistently obtain that snow transport mainly affects snow height and SMOD on the upper elevation bands and on geomorphons 'Peaks (summit)' and 'Ridge'.
The SAFRAN reanalysis dataset is freely available on the AERIS data center on the following link https://doi. org/10. 25326/37#v2020. 2. The AROME and ANTILOPE precipitation dataset can be requested online https://donneespubliques. meteofrance. fr/. The Sentinel 2 Level 3 dataset can be accessed at https://www. theia-land. fr/en/product/snow/. The raw Pleiades images are not publicly available. The 250 m averaged Pleiades images can be found at https://doi. org/10. 5281/zenodo.
//opensource. umr-cnrm. fr/projects/snowtools_git/wiki/Install_SURFEX. The version used in this work is tagged as SnowPappus-v1. 0. A user manual, describing the SURFEX namelist options related to SnowPappus is available at https://doi. org/10. 5281/zenodo. 7681340. More general information about SURFEX use can be found at https://opensource. umr-cnrm. fr/projects/snowtools_git/wiki. The code and model weights used for DEVINE are available freely at https://github. com/louisletoumelin/neural_network_and_devine/. The WhiteboxTools Open Core software used to compute the landforms classification is freely available at https://www. whiteboxgeo. com/. The  Earth System Modeling Framework (ESMF ) software is freely available at https://earthsystemmodeling. org/. The GDAL software is also freely available https://doi.
org/10. 5281/zenodo. 8340595. The DEM used in this study originates from IGN® website. Tiles can be downloaded freely at https://geoservices. ign. fr/rgealti. Forest mask originates from IGN® and can be downloaded freely at https://geoservices. ign. fr/bdforet. Waterways, lakes, and cities' spatial extent originate from IGN® and can be downloaded freely at https://geoservices. ign. fr/bdforet. The glacier masks can be downloaded freely from the Randolf glacier inventory at http://glims. colorado. edu/glacierdata/.
Code and data availability. The SnowPappus blowing snow model is developed in the framework of the open-source SURFEX project. The source files of the SURFEX system (Crocus snow model, ISBA ground model, and SnowPappus model) are provided at https://doi. org/10. 5281/zenodo. 7687821 to guarantee the permanent reproducibility of results. However, we recommend that potential future users and developers access the code from its Git repository to benefit from all tools of code management (history management, bug fixes, documentation, interface for technical support, etc. ). This requires a free and quick registration. The procedure is described at https:.
were run with and without the SnowPappus transport module and with the 3 different precipitation datasets, resulting in 6 different 250 m simulations. All simulations utilize the default options, the GM98 parametrization of blowing snow occurrence, https://doi. org/10. 5194/egusphere-2023-2604 Preprint. Discussion started: 23 November 2023 c Author(s) 2023. CC BY 4. 0 License.
Data source The UK Biobank is a large prospective study with over half a million participants aged 40-69 years recruited across England, Scotland, and Wales between 2007-2010. 16 During recruitment, participants completed questionnaires regarding socio-demographic, lifestyle and psychosocial characteristics, and had objective anthropometrics measurements, medical history and medication use recorded/ verified by trained research staff. 16 Dietary intakes were assessed using a web-based, self-administered 24-h recall which was conducted five times between 2009 and 2012. This 24-h recall has been validated against an intervieweradministered 24-h recall showing similar recordings of food items as well as estimated energy and nutrient intakes. 17 The UK Biobank received ethical approval from the North West Multi-centre Research Ethics Committee (21/ NW/0157) and data access was granted by the UK Biobank's Access Sub-committee.
Outcome measures Incident cancer cases were identified through data linkage to national cancer and mortality registries, provided by the National Health Service (NHS) Digital for participants in England and Wales, and NHS Central Register for participants in Scotland. 16 Cancer registries were coded in the 10th revision of the International Classification of Diseases (ICD-10) and the third edition of ICD for Oncology (ICD-O-3) morphology codes where appropriate, and were available up to 31 July 2019 for England and Wales, and 31 October 2015 for Scotland. Mortality registries were coded using ICD-10 and available up to 31 January 2021. Cancer deaths were defined as primary/underlying cause of death. We examined all cancers (C00-C97, except for nonmelanoma skin cancer C44) and 34 site-specific cancers.
IMD is a composite measure of deprivation for each small area of the UK based on participants' postcode, and we derived IMD quintiles based on deprivation scores. 16 Additional covariates considered in sensitivity analysis included intake of sodium, total fat, carbohydrate, red meat, processed meat, fibre, and calcium; and presence of diabetes, cardiovascular disease (angina, myocardial infarction, and stroke), depression, and hypertension at baseline. Missing data were under 3% except for physical activity (15. 1% missing) and average household income (9. 4% missing). We used multiple imputation by chained equation with 10 imputed datasets to estimate missing covariate data under assumption of missing at random and the analytical results were combined using Rubin's rule.
Statistical analysis The study cohort included 197,426 UK Biobank participants with 24-h recall data (78,330 [39. 7%], 45,137 [22. 9%], 39,841 [20. 2%], 28,654 [14. 5%], and 5464 [2. 8%] with one to five recalls, respectively) after excluding 12,680 individuals with pre-existing cancers, 173 individuals who were pregnant/unsure, and 668 individuals with an implausible total daily energy intake outside of 500-5000 kcal/day (Appendix Fig. S1 ). We compared participants' baseline characteristics by quartile of UPF consumption using analysis of variance for continuous and χ2 test for categorical variables. Furthermore, we estimated and graphically presented the mean percentage of daily food intake (or daily energy intake) for each NOVA subgroup. We used Cox proportional hazards regression with age as the underlying time metric to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between UPF consumption and each cancer outcome.
Also, the date and type of cancer incidence and mortality were ascertained through national cancer and death registries. The use of validated 24-h recalls allowed for more detailed dietary data being captured than many food frequency questionnaires, crucial for food processing classifications. 17 ur study has important limitations. First, the study cohort was not nationally representative and may overrepresent populations with white ethnicity and those living in a less socio-economically deprived areas, and the mean UPF consumption and prevalence of obesity were lower than the UK average. 30 However, this study has reported important associations comparing cancer risk and mortality by levels of UPF consumption which may still be generalisable to the wider population or similar cohorts in other contexts.
Acknowledgements This work was supported by  Cancer Research   UK C33493/A29678. Funding IIG_FULL_2020_033 was obtained from  World Cancer Research Fund (WCRF UK) , as part of the  World Cancer Research Fund International  grant programme. Where authors are identified as personnel of the  International Agency for Research on Cancer / World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the  International Agency for Research on Cancer / World Health Organization. FR declares  funding from The  São Paulo Research Foundation  ( FAPESP) 16/14302-7  and  18/19820-1. This research has been conducted using the UK Biobank Resource under Application Number  29239. The authors would like to thank the participants of the  UK Biobank study.
Acknowledgements We are grateful to all the members of the  Godinho lab  for comments and discussion of the manuscript. We thank  Edgar Gomes  for providing the KASH-DL and KASH2 constructs. P. M. and S. S. W. were supported by  Medical Research Council Grants  ( MRC ,  MR/M010414/1  and  MR/T000538/1 ). Bongwhan Yeon  was supported by a  Cancer Research UK (CRUK)  PhD studentship. S. A. G. is a fellow of the  Lister Institute  and is supported by the  MRC  ( MR/T000538/1 ). This work was supported by a  Cancer Research UK   Centre Grant  to  Barts Cancer Institute  ( C355/A25137 ). The authors declare that there is no conflict of interest.
The genetic structure is defined by finite numbers of loci and alleles per locus that determine the genetic variability of growth, maturation and reproductive effort. Climate change and fishing activities will generate selection pressures on fish life-history traits that will respond through microevolution. This paper is an overview of the Ev-OSMOSE model. To illustrate the ability of the Ev-OSMOSE model to represent realistic fish community dynamics, genotypic and phenotypic traits' mean and variance and consistent evolutionary patterns, we applied the model to the North Sea ecosystem. The simulated outputs are confronted to observed data of commercial catch, maturity ogives and length at age and to estimates of biomass for each modeled species. In addition to the evaluation of their mean value, the emerging traits' variability is confronted to length-at-age and maturity data.
The eggs are grouped in super-individuals, representing schools that are distributed spatially according to their habitat maps. During the spin-up period (until the system reaches an equilibrium), for each new school of eggs, a diploid genotype is randomly drawn from the functional and neutral pools of alleles at each locus. The mendelian transmission of genotype from parents to offspring starts at the end of the spin-up period. Input 2. 1. 5. 12 Ev-OSMOSE does not model oceanographic physical and chemical processes, but it is forced by spatially and temporally varying fields of temperature (°C) and oxygen (% of saturation) from coupled regional physical and biogeochemical models, data time series or from the regional downscaling of earth system model outputs. As for the OSMOSE model, biomass prey fields are also used as input to provide LTL.
Model calibration 2. 2. 3. The Bioen-OSMOSE-NS configuration detailed in  Morell et al. , (2023)  was calibrated to obtain estimates for unknown parameters, using maximum likelihood estimation based on an evolutionary optimization algorithm adapted to high-dimensional parameter space that is available in the calibraR R package (Oliveros-Ramos and  Shin, 2016). The algorithm explores the space of unknown parameters so as to maximize the likelihood obtained by comparing model outputs to observed data. The addition of a new evolutionary sub-model to the North Sea configuration modifies the simulation outputs of the model, notably by introducing interindividual variability through phenotypic variance, and thus the Ev-OSMOSE-NS model needed to be calibrated anew to re-estimate the same unknown parameters as in Bioen-OSMOSE-NS.
The estimations of the parameters obtained from the calibration of Bioen-OSMOSE-NS were used as initial guesses to speed up the calibration process. The calibration of the Ev-OSMOSE-NS model is an 'ecological fit' to ecological data using a model version with phenotypic variability but without genotypic transmission. The data used to calibrate Ev-OSMOSE-NS are fisheries landings (ICES, 2019a), length-at-age from scientific surveys from ICES database (NS-IBTS-Q1, ICES DATRAS 2022) and estimated biomasses for assessed species  (ICES, 2016 (ICES, , 2018a (ICES, , 2018b (ICES, , 2018c (ICES, , 2019b)). The calibration is performed for an average state of the ecosystem for the period 2010-2019 by using observed data collected over the period as target values (Supporting Information B). For each species, the estimated parameters are the larval mortality rate , the mean maximum ingestion rate , the maximum fishing mortality rate , and the additional mortality rate.
A parameter per LTL group named coefficient of accessibility of fish is also estimated. The new estimation of these parameters for the Ev-OSMOSE-NS model is given in Table  4  for species parameters and Table  5  for LTL parameters. Due to limited data on the relationship between foraging behavior, predation mortality and growth rate, the coefficients and for the trade-off between and foraging mortality were manually tuned and not calibrated through maximum likelihood estimation. They were fixed so that foraging mortality for each species was on average (i. e. when accounting for phenotypic variance in ) equal to 0. 05 and the slope was manually tuned (and hence adjusted to maintain the average at 0. 05) to obtain evolutionary trends in that were within reasonable ecological limits (results not shown obtained by activating evolution for contrary to the simulations presented here).
Emerging phenotypic variability 3. 1. Maturation 3. 1. 1. A comparison of Ev-OSMOSE-NS simulation outputs for maturity ogives with Bioen-OSMOSE-NS  (Morell et al. , 2023)  outputs and observed data can indicate whether taking into account phenotypic variance in process traits improves model realism, especially as maturity ogives were not used as targets for calibration. The maturation process can be assessed with two types of Ev-OSMOSE outputs: (i) the mean maturation age or length and (ii) the variance of the maturation age or length. The slopes of the related maturity ogives can be used to visually assess if the simulated variance better fits the observations. Compared to Bioen-OSMOSE-NS, Ev-OSMOSE-NS provides a better representation of mean age at maturity for haddock, hake, herring, plaice and sole (closer to observed mean ages at maturity), a similar one for saithe and whiting, but a worse one for cod, grey gurnard, Norway pout and sprat (vertical lines, Fig.
5A ). Ev-OSMOSE-NS outputs reproduce better observed variance in mean age at maturity for all species except sprat and mackerel (curves, Fig. 5A ). The simulated mackerel ages at maturity fail to reproduce a credible shape for the age-based maturity ogive. The evaluation of the model's maturation outputs is complemented with the length-based maturity ogives (Fig. 5B ). Those simulated with Ev-OSMOSE-NS show a much better visually fit to observed ones in terms of both mean and variance of lengths at maturity for all species except sprat. The fit to data is particularly good for haddock, herring and whiting length ogives. Figure  5 : Age-(A) and length-based (B) maturity ogives per species for observed (red), simulated without (blue) and simulated with phenotypic variance (yellow) individual data for species for which empirical maturation data are available.
Results are shown for the species for which there is enough data to estimate and plot the observed age and length maturity ogives. Age data are yearly grouped and length data are grouped by 2-centimeter classes. The vertical lines are the mean ages at maturation (A) computed as ∑ ( ) with o(a) the proportion of mature individuals at age a. The mean length at maturation is not represented. Some observed length maturity ogives are not strictly increasing and do not allow a reliable estimation of the mean maturation length. Length-at-age 3. 1. 2. The evaluation of the model on the simulated lengths-at-ages is performed in a similar way to the maturation indicators: we first inspect the shape of the length-at-age curves (Fig. 6 ) and we also calculate the sum of squared errors (SSE) between the simulated and observed means and standard deviations of length at different ages (Fig.
7 ). We chose the SSE of the standard deviations as an indicator of the goodness of fit for length variability at age, because the SSE of the variances would overly highlight outliers. The length-at-age outputs from Ev-OSMOSE-NS correctly reproduce the shape of a von Bertalanffy-like growth curve and the length hierarchy between species. Fig. 6  and 7A highlight the degree of similarity in simulations of mean length-at-age between Bioen-OSMOSE-NS and Ev-OSMOSE-NS. Ev-OSMOSE produces better results in terms of mean for herring, haddock, and plaice and fits less well for mackerel, cod, Norway pout, saithe and whiting (Fig. 7A ). A recurring trend is that the mean lengths-at-age simulated with Ev-OSMOSE fit poorly observed data for the older ages (cod, dab, grey gurnard, haddock, mackerel, sandeel, sole, sprat, whiting) while the Bioen-OSMOSE-NS results fit better at these ages.
The large SSE between simulated and observed adult length-at-age variances is also partly due to the poor data quality at the oldest ages due to a small number of samples. In addition, the data samples are collected on fish that survive until these ages: as fish experience selective pressures over their entire life (mainly fishing), we estimate the input variance of using the surviving fish, i. e. , only the surviving genotype/phenotype, which is possibly not representative of the original population diversity required as input. In other words, the poor data quality implies a poor estimate of the gonado-somatic ratio variance that results in a poor fit between simulated and observed data at older ages, as the observed length-at-age variance is probably lower than it should be. Another source of poor estimation of could come from the parameter estimation procedure where we assume that there is no covariation between and.