Supplementary material for paper "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" by Moran et al. (2025)
Bibliographic record
Abstract
Description of Contents: The large PDF file entitled “Part2_Supplementary_Material_22August2025.pdf” contains the Supplementary Material for the paper by Moran et al. (2025). Extensive use has been made of tags and links in this PDF file to make movement around the document by section, table, or figure number easier. The zip file contains 15 oversize tables that did not easily fit into the Supplementary Material PDF file. The other three PDF files contain collages of plots extracted from the supplement of monthly time series for related sulfur, oxidized nitrogen, and reduced nitrogen chemical species. These files are intended to support the interpretation and discussion of model predictions and behaviour in the paper. Citation: 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, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-4324, 2025. Related Zenodo Links: Modelling system source code: https://zenodo.org/records/13952893 2013-2016 predicted gridded concentration and acidic deposition output package: https://doi.org/10.5281/zenodo.16970403 AQ measurement data sets, model-measurement pairs, and performance evaluation tables: https://doi.org/10.5281/zenodo.16944371 Keywords: Performance evaluation, ozone forecasts, PM2.5 forecasts, chemical weather prediction, RAQDPS, GEM‑MACH, retrospective simulations, hindcasts Contact Information: mike.moran@ec.gc.ca; alexandru.lupu@ec.gc.ca
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.651 | 0.234 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".