dataesr/scientific-paragraphs-categorization huggingface dataset:
e. their orientation bandwidth). We have found that the variability of the human segmentation maps increases for images with higher uncertainty (center of  Fig 7) , that it is concentrated near areas of higher uncertainty (the boundary between textures; center and top-right panel of  Fig 7) , and that the fitted parameters, i. e. the weights placed on each orientation band, reflect the ground-truth uncertainty (i. e. integration over a broader range of orientations when uncertainty is higher; Fig  9 ). However, we emphasize that this was not meant as an exhaustive test of the hypothesis, only as an illustration of how our protocol could be used to test it. That will require collecting datasets with more trials and conditions to better constrain the parameters for individual participants, and comparing the reconstruction model used here (based on probabilistic inference) against alternative, including popular models based on feature discrimination  [59].
There are several other uses that our method and its future extensions will enable. First, as explained above, our method focuses on perceptual factors and reduces the effect of other confounders of datasets created with manual tracing. Therefore, it can be used to improve understanding of the potential biases (or lack thereof) in measuring segmentation using more traditional methods. Second, this represents an opportunity to compare contour-based segmentation (as in tracing tasks, where the participants indicate whether a pixel is a boundary of a given object, rather than the segment label of each pixel) and region based segmentation (as in our method, where the task is to compare the image regions around the two cues). Third, different from tracing tasks, our method employs a trial-based design, with precise control of cues and stimulus onset/offset.
In order to simulate OA build-up in this area, a specific model configuration set-up, adapted to the local peculiarities was necessary. As the forest is inhomogeneous, with interstitial agricultural fields, high-resolution 1 km simulations over the forest area were performed. BV OC emissions were predicted by MEGAN, but specific land cover information needed to be used, chosen from the comparison of several high-resolution land cover databases. Moreover, the tree species distribution needed to be updated for the specific conditions of the Landes forest. In order to understand the canopy effect in the forest, canopy effects on vertical diffusivity, winds and radiation were implemented in the model in a simplified way. The refined simulations show a redistribution of BV OCs with a decrease in isoprene and an increase in terpenoid emissions with respect to the standard case, in line with observations.
Measurement sites and their characteristics The data used here concern an area in and around the Landes forest in south-western France, in vicinity of the Atlantic coast. Detailed measurements were performed during the summer of 2017 (LANDEX episode 1) in a measurement site located at Salles-Bilos, the exact period of the measurements being from 20/06/2017 to 20/07/2017 depending on the availability of measurements for each studied component. More information about this site is given in the next section. Additionally, 10 air quality monitoring sites -from the ATMO national network -located around the forest have been used for validation of more common species (N O x , P M 10 , P M 2. 5 , Figure  1 ). Meteorological fields (explained in detail in section 3. 3) used in the simulations have been validated using the E-OBS database as well as the data provided by the measurement site at Salles-Bilos.
Particulate matter was also measured during the campaign. P M 10 /P M 2. 5 /P M 1 concentrations were measured using a traditional TEOM-FDMS microbalance (implemented by EPOC) and a FIDAS 200 (deployed by INERIS) analyzer, which measures total aerosol concentration in the range of 0. 18 -100 µm (with 3 measuring ranges). INERIS also utilized an aethalometer at the same time as the FIDAS in order to measure the black carbon (BC) concentrations, identifying a strong biomass burning episode (on the 05/07 to 06/07) near the measurement site, producing a peak of BC with concentrations as high as 80 µg. m -3. The simulation of fire episodes using inputs from satellite data is implemented in CHIMERE, but this option was not activated for the runs performed in this study. Therefore, and since SOA build-up from biomass burning is not the aim of this paper, this peak has been removed from the PM data produced by the FIDAS.
The measurements available for most of these stations include nitrogen oxides (N O and N O 2 ) and PM (P M 10 and P M 2. 5 ) mass concentrations. These datasets were provided by the local air quality monitoring agency, the ATMO-NA (Atmo-Nouvelle-Aquitaine, https://www. atmo-nouvelleaquitaine. org/, last accessed: 19/07/2022), information on the instruments used for the measurements is provided on their site and historic data is provided upon request. The data covers the entire period of the campaign.
CHIMERE chemistry transport model The CHIMERE offline regional chemistry transport model  (Menut et al. , 2013)  was developed initially in the early 2000s in order to simulate the concentrations of gaseous species (especially O 3 ). Later on, a module for the simulation particulate matter  (Pun and Seigneur (2007) ;  Bessagnet et al. (2008) ) was added to the model. The model is currently used extensively in air quality monitoring and forecast in research as well as for operative purposes both in regional and hemispheric levels  (Cholakian et al. (2019b) ;  Lachatre et al. (2019); Trewhela et al. (2019) ;  Lapere et al. (2020) ). It has also been used in many model intercomparison studies, as well as model-observation comparisons within multi-model experiments, for example to investigate European particulate matter trends  (Ciarelli et al.
(2016) ;  Cholakian et al. (2018) ;  Cholakian et al. (2019a) ). Boundary/initial conditions are taken from climatological simulations of LMDz-INCA3  (Hauglustaine et al. , 2014)  for gaseous species and GOCART  (Chin et al. , 2002)  for particulate matter. For each CHIMERE simulation domain, the meteorological parameters were obtained by running the WRF model (version 3. 9. 1. 1; Wang et al. (2015) ) on the same domain and with the same horizontal resolution, and using NCEP large scale input data (for Environmental Prediction/National Weather Service/NOAA/US Department of  Commerce, 2000). Several model configurations have been tested for WRF. A comparison of the model outputs with the E-OBS database (https://www. ecad. eu) and with the measurements obtained at the Salles-Bilos measurement site was conducted for all the performed meteorological runs.
mmm. ucar. edu/wrf/users/docs/ user_guide_V3/user_guide_V3. 9/ARWUsersGuideV3. 9. pdf, last accessed: 19 july 2022). The Globcover  (Arino et al. , 2008)  dataset with a resolution of 300 m×300 m was used for the land cover parameters. Bearing in mind that the forest is parcellated and that some parcels can be transformed into agricultural land, changing this database to a more recent one was considered as beneficial for this study (see 3. 4). Biogenic emissions are provided by a reduced online version of the MEGAN  (Guenther et al. , 2012)  model already having been integrated in the CHIMERE model. For the base simulations the version 2. 04 of the MEGAN model has been used. This version of MEGAN comes with pre-calculated BV OC emission factors for isoprene, α-pinene, β-pinene, limonene, ocimene, humulene and β-caryophyllene (also including the leaf area index (LAI) fields) with a horizontal resolution of 0.
008 • × 0. 008 • ; therefore, any land cover related changes in the model will not impact the BV OC emission factors. These emission factors were calculated using the Globcover land cover database. In a later sensitivity test (section 3. 4), a more recent version of the MEGAN model has been tested. Using this version, we have the possibility of recalculating the BV OC emission factors by changing the land cover, the vegetation-specific emission factors and the LAI. A more detailed explanation of this model and its use is provided in section 3. 4. Anthropogenic emissions were taken from the EMEP (EEA) emission database with a 10 km resolution for the year 2014. The emissions in this data base for the years 2014, 2016 and 2017 were compared and no major differences were observed in the levels of anthropogenic emissions between these years for this specific region (the data can be directly compared on the EMEP emissions datacenter accessible here: https://www.
ceip. at/webdab-emission-database/emissions-as-used-in-emep-models, last consulted in 13 July 2022). The reason for using the 2014 emissions is explained in detail in section 3. 5, where a local high resolution emission data base is tested. It is worth mentioning here that the CHIMERE model does not have an integrated forest canopy model; therefore, the model, while taking into account the biogenic emissions and the land cover information of the forest in different modules (for example for calculating the deposition of atmospheric species), will not take into account the dynamic physical changes introduced by the forest canopy into the concentrations of chemical species in the first two vertical layers of the simulations. These effects and the changes made in the model in order to represent them are presented in detail in section 3.
Meteorological inputs In addition to the tested WRF parameterizations, another series of meteorological inputs was tested using the ECMWF highresolution 3 hourly forecast dataset  (Owens and Hewson, 2018). The global version of the ECMWF data (with lower resolution) is used for the continental domain, while for the other two domains the high-resolution (10 km) ECMWF run is used. The comparisons of the measurements from the Salles-Bilos site to a simulation run with the ECMWF meteorological inputs is shown in Figure  2 , as well as the comparison for the WRF parameterization mentioned in section 3. 2. Before commenting on this comparison, we first briefly present the meteorological situation during the campaign which is depicted by both models in a similar way. Rainfall events (not shown in Figure  2 ) occurred between June the 26 th and July the 3 rd and also in the July 8 th to 13 th 2017 period and were associated with low solar irradiance (< 750 W.
As for the high-resolution domain, the daily mean and maximum temperature values are well simulated in both meteorological inputs. However, both models have shortcomings in the representation of daily minimum temperature values: this problem is more significant in the WRF simulations (on daily minima, a bias of +0. 22 • C and +1. 22 • C is observed in the ECMWF and WRF data respectively). The wind speed simulations are accurate in both runs, while the wind direction is more representative of the domain in the ECMWF simulations. The friction velocity (u*) shows the same pattern and is well represented in both datasets, although the night-time minimum is better represented in the WRF simulations. Precipitation and the Bowen ratio β, the ratio of heat flux to moisture flux near the surface, were also compared, and are presented in SI-3.
Land cover, tree type and tree-specific emission factors As mentioned above since the forest is parcellated and goes through annual changes, it is important to use an up-to-date source of information on forest cover and density when performing high resolution simulations. To this end, a detailed comparison of existing land cover data was conducted. It should be noted that, in most land cover databases, trees are lumped in two main functional groups: broadleaf and needleleaf trees. In addition to tree density (which varies upon the selected database), the broadleaf/needleleaf distribution is also a specific parameter of a land cover datasets. This is why, in this exercise, all databases have been examined with regard to their total tree density (this section), but also with respect to the BV OC emissions they induce, with respect to the broadleaf/needleleaf distribution (next section).
In particular, from this section on, a more recent version of MEGAN (MEGAN3. 0, Guenther et al. (  2018 )) has been used, which provide the possibility to modify the land cover and the emission factors resulting from different tree types and different regions. Therefore, all the modifications mentioned in this section were made both in MEGAN and in CHIMERE simultaneously. We performed two separate test cases in this section: one for changing the land cover data and the second for changing the regional tree species and the emission factors tied to specific trees. Both these modifications result in changes in the general BV OC emission factors; therefore, they are presented in the same section. In the first test case, we considered the datasets provided by CHIMERE and WRF and compared them with several other more recent or regionally specific data sets.
CHIMERE allows the use of two datasets for land cover information: the Globcover data  (Arino et al. , 2008)  as selected for the base simulation, and the USGS data  (Gutman et al. , 2008). As for the WRF model, it provides the option of using MODIS  (Giri et al. , 2005)  or USGS data. Also, the forest cover density information made available in MEGAN was also included in our comparisons. In addition to these data, three databases of major interest were added to our comparison. First, we considered the Copernicus database (https://land. copernicus. eu/pan-european/high-resolution-layers/forests/tree-cover-density, last accessed 29 July 2022), which provides (among other parameters) information about the tree cover density over Europe for the years 2012 and 2015 with a horizontal resolution of 20 m.
The Copernicus database also provides forest changes across Europe between the years 2012 and 2015. These changes are, on the average, about +30% forest growth in cells where the changes were positive and about -40% in cells where the changes were negative. Given these important changes, only the most recent (2015) data was considered. This database was regridded to our 1km resolution domain to have a common grid for comparisons. We also collected data from the IGN (Institut national de l'information géographique et forestière) which provides detailed information on forest density in France (via the DBForêt database) and on land cover information in general (via the DBTopo database). These data were downloaded from the IGN data portal (http://www. ign. fr/, last accessed 16 July 2022) for three administrative departments (Landes (40), Gironde (33) and Lot et Garonne (47)) in order to cover the entire Landes forest.
Both the DBForêt and the DBTopo databases have high resolution (around 20 m); temporally, the BDTopo database is for the year 2017 while the BDForêt database was updated in the year 2016 with data from the year 2014. The data for both databases were processed into a format that is usable in CHIMERE (i. e. projection of shapefiles onto a regular netcdf grid) and then  There is also a land cover dataset provided by the French national institute of agriculture, alimentation, and environment (INRAE, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement), called the Theia dataset (https: //https://www. theia-land. fr/en/homepage-en/, last accessed 15 July 2022). This dataset includes several types (reflectance, snow mask, water level, etc. ) of information gained from remote observation of the earth, including land cover data.
The dataset was 295 realized first in 2016, then updated in 2018, with a horizontal resolution of 20 m. Both datasets for years 2016 and 2018 are presented in this study (Figure  3 ). It is important to know that the base data used in order to generate the Theia datasets, apart https://doi. org/10. 5194/acp-2022-697 Preprint. Discussion started: 27 October 2022 c Author(s) 2022. CC BY 4. 0 License. from satellite information is the BDTopo database presented in the previous paragraph. Comparing the two Theia datasets (2016 and 2018) a difference of 29% is seen when conifers tree density is involved (although differences in total tree cover are small, see below). Since Theia-2018 is the most recent source of information we could get for the region at this time and since it is based on several independent data sources, it was taken as reference in Figure  3.
The upper left frame shows the 2018 Theia tree cover density for the Landes forest in shades of green. It draws a strongly delimited triangle of dense forested area on the domain, which becomes irregular in the southern part of the domain and near the coastline. The other frames show the relative difference of each data base with respect to Theia-2018. It can be seen that there are practically no differences between Theia-2018, BDTopo and Theia-2016, and only slightly lower densities in the BDForêt and MEGAN databases, while strong differences appear with the MODIS, Globcover, Copernicus and USGS data. MODIS proposes a much higher forest density than Theia, especially in and around the edge of the Landes forest. A possible reason for this could be the data being outdated (the MODIS version used here dates from 2000) or the lower resolution of MODIS (1 km×1 km) compared to the others.
The Copernicus data base shows a consistent underestimation of the forested area over the Landes forest, as does the Globcover database to a lesser degree; probably because its resolution is higher than MODIS, but it is still outdated (2000s). The USGS data base shows either a strong overestimation or an underestimation for different 1km grid cells. Since this database is quite old, the changes between forest and agriculture could be responsible for these discrepancies. From this point on, the Theia-2018 land cover database has been used both in CHIMERE and in MEGAN. The second step of this section is to analyze the estimation of the BV OC emission factors when tree types in the region and tree-specific emission factors are changed in MEGAN. The BDForêt database mentioned above provides more detailed information about distribution of tree species inside the Landes forest as well as their density.
It was considered necessary to modify the tree type distribution in the MEGAN model since MEGAN 2. 1 assumes 28% of maritime pine coverage for the Landes forest, while the BDForêt database shows around 95% of the same species. The reason for this discrepancy is that MEGAN considers an average tree species distribution for western European forests up to 300 km from the Atlantic coast, which does not take into account the specificity of the Landes forest. Therefore, modifications were made in the MEGAN v3 model inputs in order to i) create a specific ecosystem for the Landes forest containing 95% of maritime pines and ii) modify the emission factors for the maritime pine to experimental values obtained during the Landex campaign (E. Ormeno, personal communication). The resulting emission factors were integrated into CHIMERE and a sensitivity case was run with this new configuration.
For the region being studied here it is important to notice that the effect of changing the type of trees and the experimental emission factors for these trees is more important for the emission of monoterpenes, while the change caused by modifying the land cover seems to be more important for isoprene emissions. It should be taken into account that, a test was run by replacing the LAI used in the model by the Copernicus 1 km horizontal resolution global LAI-v2 databases (accessible here: https://land. copernicus. eu/global/products/lai, last accessed July 2022) for the year 2017, leading to no significant changes in the results of the simulations compared to when the LAI implemented in CHIMERE (from MEGAN 3. 0) is used.
This 5 th sensitivity case revolves around changing the anthropogenic emissions from the reference EMEP emissions to the emissions provided by ATMO-NA. This modification, in addition to providing anthropogenic emissions with greatly increased horizontal resolution, has the advantage of being prepared from bottom-up data specific to this area. The comparison of these two emission datasets is shown in SI-4. In summary, the two databases show comparable emissions for all species, presenting the highest average bias for SO x of around 20%, dominant SNAP sectors being always the same for each species group between the two emission inventories. It should be noted that the emissions provided by ATMO-NA are relative to the year 2014; therefore, in order to have consistent emission inventories, we used the 2014 EMEP emission inventory for this test.
This simulated value can thus be observed at 6 m height at Salles-Bilos. According to visual inspection of the measurement site, most of the LAI is distributed in the last meters of the pine trees in that specific patch of the forest. This equation is also used on cells with higher than 70% tree density. It is also important to take into account that the swrd changes affect only the chemistry modules of the model, by reducing photolysis frequencies. On the contrary, emissions of BV OCs are not affected by these modifications, since the effect of the canopy on the diminution of swrd is already taken into account in the MEGAN model when calculating the BV OC emission factors. These modifications were added in 2 steps: first the K z and the wind speed modifications and second the swrd modification.
also like to acknowledge the E-OBS dataset and the data providers in the ECA&D project (https://www. ecad. eu). Copernicus land data is acknowledged for the European forest dataset and also for leaf area index datasets which were generated using Copernicus Atmosphere Monitoring Service Information 2018. Neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains. IGN (https://www. ign. fr/, last accessed July 2022) is acknowledged for BDTopo and BDForet datasets.
Acknowledgements. The authors would like to acknowledge the  University of Bordeaux , the  CNRS   INSU LEFE program  and the  ANR  in the frame of the Investments for the future Program, within the Cluster of  Excellence COTE  ( ANR-10-LABEX-45 ) for their financial support. This work was granted access to the  HPC resources of IDRIS  under the allocation  2019-gen7232  and  2021-gen10274  made by  GENCI. ATMO-NA (https://www. atmo-nouvelleaquitaine. org/, last accessed July 2022) is also acknowledged for both the air quality station measurements and the local anthropogenic emission dataset ( Inventaire Atmo Nouvelle-Aquitaine   2014 -ICARE v3. 2. 1_rev1 ). They would.
Code and data availability. The model is available for download on the CHIMERE website (https://www. lmd. polytechnique. fr/chimere/ 2020_getcode. php). The WRF model is downloadable on their website (https://github. com/wrf-model/WRF). ECMWF data are available for download for registered members through request. The measurements can be provided by coauthors upon request. Simulation output will be available upon request. All the rest of the inputs/data used in the article are all downloadable free of charge on their respective websites mentioned in the text.
Experimental Animals To visualize the morphology of microglia with 2-photon microscopy, we used heterozygous Cx3cr1-EGFP transgenic mice of both sexes expressing enhanced green fluorescent protein (EGFP) knocked into the Cx3cr1 locus, which is specific for microglia, macrophages and monocytes  [19]  in C57BL/6J background. To visualize the morphology of astrocytes, we used hGFAP-ECFP transgenic mice expressing enhanced cyan fluorescent protein (ECFP) under the control of the human GFAP promoter which is specific for astrocytes in an FVB/N genetic background  [20]. To a priori estimate the number of mice needed for intravital imaging experiments, we used the G*Power software, setting as effect size the microglial/astroglia cell body volume, and determined that a sample size of 5 animals per experimental group was needed to extract statistically significant results.