Implementing and Improved Soil NOx Parameterization in the Community Multiscale Air Quality Model: Implications for Air Pollution
Bibliographic record
Abstract
Soil NO emissions are critical to accurate atmospheric simulations which inform decisions to protect human health. Several studies indicate that the scheme, Yienger and Levy 1995 (YL95), underestimates soil NO emissions by a significant amount. The Berkeley Dalhousie Soil NOx Parameterization (BDSNP) updates soil NO emissions to be more consistent with satellite measurements. This work implements the BDSNP algorithm into CMAQ, adapting it to a 12km grid and comparing the resulting ozone, particulate, and other pollutants with results from the current YL95 algorithm for 2005 satellite data. Results show that summer NO emissions over the US double during the day, and in some places soil NO exceeds industrial sources. A comparison with satellite data, however, does not show strong evidence of the YL underestimation, contrary to other published results.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".