Adjoint estimation of ozone climate penalties
Bibliographic record
Abstract
Abstract An adjoint of a regional chemical transport model is used to calculate location‐specific temperature influences (climate penalties) on two policy‐relevant ozone metrics: concentrations in polluted regions (>65 ppb) and short‐term mortality in Canada and the U.S. Temperature influences through changes in chemical reaction rates, atmospheric moisture content, and biogenic emissions exhibit significant spatial variability. In particular, high‐NOx, polluted regions are prominently distinguished by substantial climate penalties (up to 6.2 ppb/K in major urban areas) as a result of large temperature influences through increased biogenic emissions and nonnegative water vapor sensitivities. Temperature influences on ozone mortality, when integrated across the domain, result in 369 excess deaths/K in Canada and the U.S. over a summer season—an impact comparable to a 5% change in anthropogenic NOx emissions. As such, we suggest that NOx control can be also regarded as a climate change adaptation strategy with regard to ozone air quality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".