dataesr/scientific-paragraphs-categorization huggingface dataset:
naiades. eaufrance. fr/acces-donnee). This database contains the results of analyses performed by water agencies and environmental consultancies targeting selected active substances, 28 of which were present in the database on pesticide purchases. For each of these substances and for each location of water analysis, we averaged the quantity of the active substance measured in the water over all samples collected in 2017. For each substance and postcode, we averaged measured quantities (mg/L) across all sampling sites located in the postcode. As the pesticide residues sampled in the water of one postcode could originate from neighbouring postcodes, we weighted the quantity of substances sampled in a postcode based on the samplings in the neighbour postcodes in a buffer of 10km. For the 28 active substances common to both databases, we calculated the Pearson correlation between the amounts purchased and the quantity measured in water bodies in the postcode and the 10km-buffer neighbourhood.
Of these 28 substances, we found a mean correlation of 0. 28 (range: -0. 05 ; 0. 68 ; Figure  C  and Table  A ) between the Naïade database and the BNV-d database. Although not very strong, such correlations suggest that the pesticide purchase data we used are indeed related to environmental contamination. We agree that further investigation are needed to ascertain the links between pesticide purchase and environmental contamination but this might need further data to be fully informative. For example, depending on substance characteristics, such as degradability, and the way they are used, such as weather conditions during spraying, we could expect variations in the level of environmental contamination. A such, we propose not to include this preliminary analysis in the manuscript but stress the need for further detailed analysis (P22 l.
The power of this study is that this approach will allow ecotoxicologists to study mixtures of pesticides to which a given species is likely exposed in a given part of its distribution range. The approach taken by the authors is sufficiently well described to be applied by others in other regions and countries where a database of pesticide sales is kept. From an ecotoxicological perspective, this study is exciting and opens new avenues to research on the impacts of pesticide use at the landscape level. The approach could even be coupled with georeferenced data on biodiversity to further orient studies of the effects of these mixtures. I do not have the competence to make a judgment on the statistical approach, and that part will need to be assessed by someone with the relevant expertise. Finally, the style is clear and the English is excellent.
Limitations of the Study Our study describes the PyShell in two model diatom species and demonstrates how this Rubisco-encasing protein barrier is essential for pyrenoid architecture and function in T. pseudonana. However, our data has some limitations that raise new questions requiring future in-depth investigation: 1) PyShell structure: The N-terminal 68 residues in TpPyShell1 are mostly disordered and were not resolved in our in vitro SPA structure. When mapping the unrolled SPA model into our in situ STA density (Figure  3F ), we noted extra density beyond each monomer's single α-helix. STA would require sub-nm resolution to distinguish whether this extra density is constituted by the PyShell lattice, itself, or rather comes from an interacting protein. This ambitious goal faces the challenges of resolving small proteins in situ  (Russo et al.
Cryo-EM grid prep and data acquisition 3. 0 µL of TpPyShell solution was applied to a glow-discharged Quantifoil holey carbon grid (R1. 2/1. 3, Cu, 200 mech), blotted for 3. 5 sec at 4ºC and plunge-frozen into liquid ethane using a Vitrobot Mark IV (Thermo Fishrer Scientific). The grid was inserted into a Titan Krios (Thermo Fishrer Scientific) operating at an acceleration voltage of 300 kV and equipped with a Cs corrector (CEOS, GmbH). Images were recorded with a K3 direct electron detector (Gatan) in CDS mode with an energy filter at a slit width of 20 eV. Data were automatically collected using SerialEM software  (Mastronarde, 2005)  at a physical pixel size of 0. 87 Å, with 52 frames at a dose of 0. 96 e -/Å per frame, an exposure time of 2. 63 sec per movie, and defocus ranging from -0.
FIB-SEM data acquisition and analysis Sample preparation was performed as in  (Uwizeye et al. , 2021). FIB-SEM tomography was performed with a Zeiss CrossBeam 550 microscope, equipped with Fibics Atlas 3D software for tomography. The voxel size was 16x16x16 nm for the WT, 6x6x6 nm for the m1 mutant, and 8x8x8 nm for the m2 mutant. The whole volumes were imaged with an average of 300 frames for WT and 1000 frames for the mutants. Single cells were isolated by cropping in 3D using the open software Fiji  (Schindelin et al. , 2012). Image misalignment was corrected using the "StackReg" plugin in Fiji. We used 3D Slicer for segmentation and 3D reconstruction, and Meshlab to reduce noise and enhance contours of reconstructed objects. The quantitative measurements of chloroplasts and pyrenoids organelles (volume, diameter, sphericity) was implemented in python using libraries including trimesh, stl and scikit-image.
Acknowledgements: We thank  Vandana Tomar Deopa  and  Kohei Yoneda  for helping establish the mutant strains, as well  Nobuko Higashiuchi  and  Eri Nakayama  for technical assistance. We thank  Cécile Giustini  for and  Haythem Hsine  for data analysis and strain maintenance,  Glen Wheeler  and  Colin Brownlee  ( Marine Biological Association of the UK ) for supporting the first cryo-ET investigations, and  Caitlyn McCafferty  for help with bioinformatic analysis. We thank  Rikuya Morimoto ,  Kanae Fukushima  and  Taiki Fukuzawa  for their help establishing the preparation of recombinant PyShell protein, as well as  Atsunori Oshima  and  Yoshinori Fujiyoshi  ( Naogo University ) for TEM instrumentation access at the beginning of the SPA cryo-EM study. We thank  Guy Schoehn  and  Christine Moriscot  ( IBS/ISBG electron microscope facility ;  Grenoble Instruct-ERIC center ) for access and assistance with FIB/SEM instrumentation.
Data availability: Single particle cryo-EM maps (EMD-37751), cryo-ET subtomogram averages (EMD-18709), and cellular tomograms (EMD-18710 to EMD-18713) are available in the Electron Microscopy Data Bank. Atomic models of the PyShell structure are deposited at the Protein Data Bank (PDB-8WQP). Raw cryo-EM data (EMPIAR-11724) and raw cryo-ET data (EMPIAR-11747) used to generate the density maps are available on the Electron Microscopy Public Image Archive. The PyShell nucleotide sequences reported in this paper have been deposited in DDBJ/EMBL/GenBank under accession numbers XM_002179781, XM_002185069, OR682719, OR682720, XM_002292359, XM_002292147, XM_002292148. The proteomics data is available at ProteomeXchange (PXD041920) and jPOST (JPST001940). Strains and plasmids generated in this study are available from the corresponding authors upon request.
Data collection The survey was distributed in Italy, Spain and Portugal through social networks and on campus questionnaire dissemination. Although, we note that a small part of the data was collected from people who live in other countries but speak Italian, Spanish and Portuguese, these data was still worthwhile to be used since either people originated from these three countries or those who speak these three languages as their first language are considered to have the corresponding cultural, local and dietary backgrounds. In the end, 2,171 answers including 46. 7% Italian data, 31% Portuguese data and 22. 3% Spanish data were collected, and the demographic information is detailed in Tables  1, 2.
Statistical analysis The data was analyzed using R software (version. 4. 1. 1) and IBM SPSS 25 depending on the different output needs such as plotting. The demographic variables and their categories are presented in Tables  1, 2. Some treatments of categories need to be specified such as for age, 18-30 years of age was considered as young, 31-50 years of age was considered as middle-aged and more than 51 years of age was considered as senior or old in this study. For meat consumption, people never eat meat were considered as vegans and/or vegetarians. These treatments were used in the following analysis with General Linear Model (GLM) and logistic regression modeling. Three types of statistical analyses were performed with this transformed data set. First, variance analysis (ANOVA) was performed with the GLM procedure in SPSS as previously described  (27, 29)  to examine the difference in WTT, WTE, and WTP depending on respondent groups based on different demographic characteristics.
As in previous studies  (27) , some, but not all of the assumptions of ANOVA were sometimes violated in this survey case, such as normality of distributions and homogeneity of variances. Therefore, we ran a Welch's ANOVA, which does not require the homogeneity of variance assumption, and we obtained extremely similar results compared to ANOVA which is considered as being robust  (30). Based on these observations, we proceeded with ANOVA since the Welch's ANOVA does not accept interactions. Post-hoc test was performed using Bonferroni test with the Bonferroni correction for pairwise comparisons between groups with significant difference, which was determined at the level of p < 0. 05. Second, to identify the potential motives and barriers to the acceptance of CM, Pearson correlation analysis was thereafter performed by R software to determine the relationships between variables (WTT, WTE, WTP, and other questions regarding the perception of conventional meat production and of CM).
25 × answer to question 1 + 0. 25 × answer to question 2 + 0. 25 × answer to question 3 + 0. 25 × answer to question 4. Third, logistic regression was developed to identify barriers and motives to accept CM. As demonstrated by Verbeke et al. (31) , since this novel product is not yet commercially available on a large scale and to be consumed frequently, it is difficult to obtain real data regarding consumer WTT and WTE. Nevertheless, consumer willingness is highly driven by the perception of the product, and emotional resistance to the concept of CM can negatively affect consumers' perception of this product and their willingness to try and eat  (27, 29). Combined with relevant demographic variables, this analysis considered also the variability in emotional resistance, allowing to investigate the potential profile of CM adopters and rejectors.
Limitation concerning sampling and representativeness In general, the sample collected for this study consisted of a slightly more female, young, middle-aged and Italian population. It is certainly the most rigorous and correct approach to collect demographically representative data for a questionnaire-type study. Nevertheless, are survey data that are less strictly representative can be effective to convey some useful information? In recent years, there has been a proliferation of research studies on CM, using sample data that are either representative of the local population  (43)  or not, which seem to be more often not representative  (22, 31). However, through our research, as well as previous research and review articles  (48) , the perceptions and acceptance of CM among consumers with different origins and backgrounds, while not identical, but basically move up and down on the same trends.
, 2020)  are written in Python and take advantage of state-of-the-art computational libraries such as Iris  (Met Office, 2010 -2013)  and methods such as parallelization and out-of-core computation (Dask; Dask Development Team, 2016) to allow for efficient and user-friendly data processing. Common operations on the input data such as horizontal and vertical regridding, masking of missing values across different datasets, or computation of multi-model statistics are centralized in a highly optimized preprocessor and available to all diagnostics. ESMValTool is mainly controlled by so-called "recipes", which are userdefined YAML files (YAML Language Development Team, 2021) that specify the main workflow of ESMValTool. Originally, ESMValTool was designed and applied to process and analyze the output from CMIP models (e.
g. ,  Bock et al. , 2020). For this, the model output has to be formatted according to the CMIP data request (https://clipc-services. ceda. ac. uk/dreq/index. html, last access: 1 November 2022,  Juckes et al. , 2020)  and the Climate and Forecast (CF) conventions  (Eaton et al. , 2022)  regarding variable names, metadata, and file format. Usually, this is done with the Climate Model Output Rewriter (CMOR; see  Mauzey et al. , 2022)  based on the CMOR tables (e. g. , https://github. com/PCMDI/ cmip6-cmor-tables, last access: 1 November 2022). This process is usually referred to as "CMORization" and the reformatted data can be described as "CMORized". While this has become a quasi-standard for large model intercomparison projects such as CMIP, this hampers application of ESM-ValTool during model development cycles or for monitoring of running model simulations as "native" model output typically does not follow the CMOR standard and thus would have to be CMORized in an additional step before running ESMValTool.
This function takes all variables of a file as an input. Finally, fix_data is applied to datasets after extracting the desired time ranges from the input files and concatenating them into a single object. This function takes only the desired variable as an input and contains potentially time-consuming fixes that should not be applied to all input files but rather only to the subset of data requested by the user. However, in practice, most fixes only use fix_metadata even when the actual data needs to be modified. The reason for this is the different call signatures of fix_metadata and fix_data: while fix_metadata takes all available variables of the input files as input, fix_data only uses the single requested variable. An example where this is necessary is the variable derivation mentioned above, in which a CMOR variable is calculated from one or multiple other variables present in the input files.
, project: ICON for the ICON model. Note that while for projects like CMIP6 or OBS the key dataset refers to the name of the model or observational product, for native model output it refers to a sub version of the model or simply repeats the name from the project, e. g. , dataset: ICON for the ICON model. Due to technical reasons, it is not possible to omit the key dataset, although it may be redundant in some cases. To facilitate the handling of native model output, ESM-ValTool now also allows the automatic addition of extra facets to the variable metadata (top green box on the left of Fig. 1 ). The term "facet" here refers to key-value pairs that describe datasets requested by the user in an ESMValTool recipe, e. g. , project: CMIP6, dataset: CanESM5, mip: Amon, exp: historical, or short_name: tas.
These extra facets are automatically added to the original facets (if not already present) depending on the project, dataset name, MIP table, and variable requested by the user. By default, extra facets are read from a YAML file contained in the ESMValTool repository. If needed, a custom location for this file can be specified by the user. An example of extra facets for the EMAC model is given in Appendix A. In the context of reading native model output, extra facets can be used to locate input data. For example, if native model output is structured in subdirectories, the name of the corresponding subdirectory for each variable can be conveniently added through extra facets. This avoids the necessity to include this information in the ESMValTool recipe, and the users do not need to be familiar with the peculiarities of each model.
Simulation of dynamic vegetation processes is performed by LPJ-GUESS  (Smith et al. , 2014; Lindeskog et al. , 2013). Aerosols and chemical processes are described by  TM5 (van Noije et al. , 2021)  and the Greenland ice sheet is modeled using PISM  (Bueler and Brown, 2009; Winkelmann et al. , 2011). The coupling of all components is performed using the OASIS3-MCT coupling library  (Craig et al. , 2017). EC-Earth3 produces output in netCDF format for the ocean and the sea ice domains, and in GRIB format for the atmosphere and land domains. As part of the standard workflow used to run the model, these data are then post-processed to a CMOR-and CF-compliant netCDF format. For this, the Python package ece2cmor3 (van den Oord, 2017) is used, which contains modules to format output from each of the model components.
Thus, with CHANNEL, a set of model variables (called "objects") are grouped into a "channel". Each channel is output at a user-defined frequency as a (time) series of files. Different channels can be output with different frequencies, and objects can be part of multiple channels. To reformat EMAC data (most commonly provided in netCDF format), many variable-specific fixes are required since a large number of CMOR-type variables are not directly present in the native model output but need to be derived from other variables. For example, the variable "pr" (total precipitation) is calculated as the sum of the large-scale precipitation, convective precipitation, and snowfall. Consequently, a rather large amount of information needs to be provided in the form of extra facets. This includes raw variable names used in EMAC output files (only necessary if they differ from their corresponding CMOR variable names) and information on the EMAC channel (see above).
If the grid information (latitude and longitude coordinates) is missing in an input file, which can be the case for ICON output depending on the model settings, it is automatically added during the CMOR-like reformatting using the corresponding grid file. This grid file is specified in the global netCDF attributes of the ICON file and is automatically downloaded from MPI-M servers if necessary. For the vertical grid, the ICON reformatting supports the terrainfollowing hybrid sigma height coordinates that are used by the ICON model  (Giorgetta et al. , 2018)  but also a regular height coordinate that simply describes the altitude of the grid cells. If available in the input file, pressure levels (including bounds) are added to the ESMValTool output files. To be able to compare native ICON output directly with other models, observational products, or reanalysis data, an additional preprocessing step is usually necessary to interpolate the ICON data to a regular grid.
This can be done with ESMValTool's regridding preprocessor, which is described in detail in Sect. 3. However, ICON data can also be regridded by external tools like CDO (Climate Data Operators;  Schulzweida, 2021)  if needed by the user, since the CMORlike reformatting also supports ICON data on regular grids. For example, if users require a regridding algorithm available in CDO but currently not supported by ESMValTool, the native model data can be regridded using CDO in an additional post-processing step after running the model before being processed by ESMValTool.
IPSL-CM6 IPSL-CM6A-LR (hereafter IPSL-CM6) is an ESM developed by the Institut Pierre-Simon Laplace Climate Modeling Center. It is composed of the LMDZ atmospheric model version 6A-LR  (Hourdin et al. , 2020) , the ORCHIDEE land surface model  (Krinner et al. , 2005)  version 2. 0, and the NEMO ocean model  (Madec, 2008 (Madec, , 2015)). The latter is based on the stable version 3. 6 of NEMO, which includes three major components: the ocean physics model NEMO-OPA  (Madec et al. , 2017) , the sea ice dynamics and thermodynamics model LIM3  (Vancoppenolle et al. , 2009; Rousset et al. , 2015) , and the ocean biogeochemistry model PISCES  (Aumont et al. , 2015). IPSL-CM6 uses the XIOS input/output system (Meurdesoif, 2017), which combined with dr2xml (https://github. com/rigoudyg/dr2xml, last access: 1 November 2022) allows production of CMOR-compliant output directly at run time.
Since native IPSL-CM6 output consists of netCDF files that comply to other conventions like CF, only a small number of ESMValTool fixes is necessary for the CMOR-like reformatting of the data. Apart from common fixes that are applied to all native model datasets (adapting variable and coordinate metadata and the addition of scalar coordinates), a fix for an auxiliary time coordinate that is not CMOR-compliant needs to be applied. Extra facets for IPSL-CM6 include raw variable names used in the native IPSL-CM6 output and information about the variable groups and directories used to store the corresponding variables.
Regridding data on unstructured grids Many state-of-the-art ESMs do not use rectilinear or curvilinear horizontal grids for the spatial discretization but instead use unstructured grids. Unstructured grids are usually described by a list of all grid cells using a single spatial dimension. For each grid cell in this list, latitude and longitude values for the central points (representative for the cell "face") and bounds (cell "nodes") are specified by additional variables. Grid cells of unstructured grids usually consist of polygons whose number of vertices is different than four. For example, the ICON model (see Sect. 2. 2. 4) uses triangular grid cells. Unstructured grids offer numerical advantages in terms of scalability and computational efficiency and also often offer a more straightforward implementation of multiresolution modeling (e.
g. , nested high-resolution grids in regions of interest). However, the evaluation of native model output on unstructured grids is challenging: for example, the output of most observations or reanalyses is given on different (regular) grids (which complicates a direct comparison) and most ESMValTool diagnostics therefore expect data on regular grids. For this reason, a regridding preprocessor that is able to interpolate unstructured grids to regular grids is often crucial for evaluation of such native model output. Currently, ES-MValTool provides three different regridding schemes that allow regridding from unstructured grids to regular grids: nearest-neighbor, bilinear, and first-order conservative inter-polation. While the first scheme supports unstructured data in arbitrary format (the only prerequisite is the existence of latitude and longitude coordinates), the latter two can only be used with data that follows the UGRID (Unstructured Grid) conventions  (Jagers et al.
, 2016). UGRID provides a systematic description of the topology of unstructured meshes (e. g. , it clearly defines the connectivity between the cell faces and nodes), which is necessary to perform the more complex regridding operations. Nearest-neighbor interpolation is natively supported by Iris used in the ES-MValTool preprocessor. Bilinear and first-order conservative regridding are supported via the iris-esmf-regrid package (https://github. com/SciTools-incubator/iris-esmf-regrid, last access: 1 November 2022), which collects and provides the Earth System Modeling Framework (ESMF; https: //earthsystemmodeling. org/regrid/, last access: 1 November 2022) regridding schemes for Iris. The use of iris-esmf-regrid is possible due to an extension of ESMValTool's regridding functionalities that allows the usage of external regridding packages (in addition to native Iris schemes) with arbitrary options.
An example of regridding ICON data on an unstructured grid is illustrated by Fig. 2. Figure  2a  shows the triangular grid cells of the native model output on an R2B4 grid with a horizontal resolution of about 160 km. Figure  2b  shows the data interpolated on a regular 2 • × 2 • grid that has been regridded using ESMValTool's nearest-neighbor scheme. From a visual inspection, both fields are very similar. As an additional sanity check, we calculated the global mean near-surface air temperature for both grids, which gives almost identical values of 287. 14 and 287. 16 K for the native grid and the interpolated grid, respectively. Since native ICON output does not follow the UGRID conventions, only the nearest-neighbor scheme is currently supported for this model. However, in ESMValTool v2.