CMAQ v5.5 Annual 2023 Gridded Predictions Across the US and Canada (v1.0)
Bibliographic record
Abstract
CMAQ v5.5 Annual 2023 Gridded Predictions Across the US and Canada Data contact: Havala Pye, ORCID: 0000-0002-2014-2140 This dataset provides daily predictions of ozone and fine particle (PM2.5) species across the contiguous U.S. and a large fraction of Canada at 12km horizontal resolution for 2023. Values are predicted by CMAQv5.5 with CRACMM chemistry. Please see Pye, H. O. T. (2025). CMAQ v5.5 Annual 2023 Gridded Predictions Across the US and Canada (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15732714 for the full archive. Please cite the following for CMAQ with CRACMM2: Skipper, T. N., D'Ambro, E. L., Wiser, F. C., McNeill, V. F., Schwantes, R. H., Henderson, B. H., Piletic, I. R., Baublitz, C. B., Bash, J. O., Whitehill, A. R., Valin, L. C., Mouat, A. P., Kaiser, J., Wolfe, G. M., St. Clair, J. M., Hanisco, T. F., Fried, A., Place, B. K., and Pye, H. O. T.: Role of chemical production and depositional losses on formaldehyde in the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM), Atmos. Chem. Phys., 24, 12903–12924, https://doi.org/10.5194/acp-24-12903-2024, 2024. DISCLAIMER: This data product has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for public release. At the time of release, the data had not yet been published in peer-reviewed literature. The data is provided for research and the user should verify the data is suitable for their intended use.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.020 |
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".