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Record W7081684021 · doi:10.5281/zenodo.16970402

RAQDPS023 Predicted 2013-2016 and 2021/22 Seasonal and Annual Dry, Wet, and Total Acidic Deposition Fields and Related Concentration Fields for North America

2025· dataset· en· W7081684021 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsDeposition (geology)GridArcticArchipelagoClimate changeAir pollutionGrid cellProjection (relational algebra)Air quality index

Abstract

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Description This data set contains model-predicted, gridded seasonal and annual dry, wet, and total acidic deposition fields and related concentration fields for North America for five years: 2013-2016 and June 2021‒May 2022. As described by Moran et al. (2026b) these fields were generated by Environment and Climate Change Canada’s Regional Air Quality Deterministic Prediction System version 023 (RAQDPS023) using a regional configuration of the Global Environmental Multiscale‒Modelling Atmospheric CHemistry (GEM-MACH) online chemical weather forecast model. The regional domain considered covers most of North America, stretching from northern Mexico in the south to the Canadian Arctic archipelago and Alaska in the north. The map projection used is a rotated latitude-longitude projection (see below for details about the coordinate reference system). The domain is a subset of the Yin global grid (Moran et al., 2026a). The horizontal grid size was 728 (W-E direction) by 598 (S-N direction) and the horizontal grid spacing employed was 0.09⁰ (~10 km). For the vertical grid 84 vertical levels from the Earth’s surface to 0.1 hPa were considered on a hybrid coordinate system. Year-specific anthropogenic emissions were input by GEM-MACH for each of the five annual simulations, where continental anthropogenic SO2 and NOx emissions declined over this near-decadal period by 60% and 36%, respectively. Note that wildfire and lightning emissions were not considered in these simulations. Two closely related versions of the GEM-MACH model were used to generate these fields. Version 3.1.0.0 of GEM-MACH (https://zenodo.org/records/15330612), which has been described in detail by Moran et al. (2026a), was used for the 2021/22 simulation while version 3.1.1.2 (https://zenodo.org/records/13952893), whose code and configuration were algorithmically identical, was used for the 2013-2016 simulations (Moran et al., 2026b). There are 51 files in this data set: a documentation file called "README.pdf" and 50 NetCDF data files, 25 for seasonal and annual mean concentration fields and 25 for seasonal and annual accumulated deposition fields. Plots of some of these fields can be found in Moran et al. (2026b): see Figures 1, 5, 13, 14, 18, S10, S11, S14‒S17, S22‒S24, and S31‒S36. Monthly, seasonal, and annual evaluation scores for the predicted wet concentration and wet deposition fields for 2013-2016 can also be found in Moran et al. (2026b). References Moran, M. D., Savic-Jovcic, V., Stroud, C. A., Ménard, S., Gong, W., Zhang, J., Zheng, Q., Chen, J., Akingunola, A., Lupu, A., Menelaou, K., and Munoz-Alpizar, R.: Operational chemical weather forecasting with the ECCC online Regional Air Quality Deterministic Prediction System version 023 (RAQDPS023) – Part 1: system description, Geosci. Model Dev., 19, 4137–4203, https://doi.org/10.5194/gmd-19-4137-2026, 2026a. Moran, M. D., Lupu, A., Savic-Jovcic, V., Zhang, J., Zheng, Q., Boutzis, E. I., Mashayekhi, R., Stroud, C. A., Ménard, S., Chen, J., Menelaou, K., Munoz-Alpizar, R., Kornic, D., and Manseau, P. M.: Operational chemical weather forecasting with the ECCC online Regional Air Quality Deterministic Prediction System version 023 (RAQDPS023) – Part 2: Multi-year prospective and retrospective performance evaluation, Geosci. Model Dev., 19, 4205–4270, https://doi.org/10.5194/gmd-19-4205-2026, 2026b. Coordinate Reference System Descriptions WKTGEOGCRS["Rotated_pole", BASEGEOGCRS["unknown", DATUM["unnamed", ELLIPSOID["Sphere",6370997,0, LENGTHUNIT["metre",1, ID["EPSG",9001]]]], PRIMEM["Greenwich",0, ANGLEUNIT["degree",0.0174532925199433, ID["EPSG",9122]]]], DERIVINGCONVERSION["Pole rotation (netCDF CF convention)", METHOD["Pole rotation (netCDF CF convention)"], PARAMETER["Grid north pole latitude (netCDF CF convention)",31.7583124544932, ANGLEUNIT["degree",0.0174532925199433, ID["EPSG",9122]]], PARAMETER["Grid north pole longitude (netCDF CF convention)",87.597031302933, ANGLEUNIT["degree",0.0174532925199433, ID["EPSG",9122]]], PARAMETER["North pole grid longitude (netCDF CF convention)",0, ANGLEUNIT["degree",0.0174532925199433, ID["EPSG",9122]]]], CS[ellipsoidal,2], AXIS["latitude",north, ORDER[1], ANGLEUNIT["degree",0.0174532925199433, ID["EPSG",9122]]], AXIS["longitude",east, ORDER[2], ANGLEUNIT["degree",0.0174532925199433, ID["EPSG",9122]]]] Proj4+proj=ob_tran +o_proj=longlat +o_lon_p=0 +o_lat_p=31.7583124544932 +lon_0=267.597031302933 +ellps=sphere +no_defs +type=crs NetCDFdimensions:rlat = 598 ;rlon = 728 ;string1 = 1 ;variables:float lon(rlat, rlon) ;lon:_FillValue = NaNf ;lon:standard_name = "longitude" ;lon:long_name = "longitude" ;lon:units = "degrees_east" ;lon:_CoordinateAxisType = "Lon" ;lon:axis = "X" ;float lat(rlat, rlon) ;lat:_FillValue = NaNf ;lat:standard_name = "latitude" ;lat:long_name = "latitude" ;lat:units = "degrees_north" ;lat:_CoordinateAxisType = "Lat" ;lat:axis = "Y" ;float rlon(rlon) ;rlon:_FillValue = NaNf ;rlon:standard_name = "projection_x_coordinate" ;rlon:long_name = "longitude in rotated pole grid" ;rlon:units = "degrees" ;rlon:axis = "X" ;rlon:descriptive_name = "grid_longitude" ;float rlat(rlat) ;rlat:_FillValue = NaNf ;rlat:standard_name = "projection_y_coordinate" ;rlat:long_name = "latitude in rotated pole grid" ;rlat:units = "degrees" ;rlat:axis = "Y" ;rlat:descriptive_name = "grid_latitude" ;char rotated_pole(string1) ;rotated_pole:grid_mapping_name = "rotated_latitude_longitude" ;rotated_pole:earth_radius = 6370997. ;rotated_pole:grid_north_pole_latitude = 31.7583124544932 ;rotated_pole:grid_north_pole_longitude = 87.597031302933 ;rotated_pole:_Encoding = "utf-8" ;

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.614
Threshold uncertainty score0.767

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.205
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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