Understanding ozone‐meteorology correlations: A role for dry deposition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Observations of coincident high relative humidity and low surface ozone are common in air quality data sets, but models underpredict the strength of this correlation. We perform a statistical analysis of 28 years of ozone and meteorology observations taken as part of the Clean Air Status and Trends Network across the United States and find that vapor pressure deficit (VPD) is the strongest predictor of midday ozone in the spring, summer, and fall, and this correlation is strongest at sites with the largest leaf area index. We argue that stomatal regulation of dry deposition, which is known to have a VPD dependence that is not typically included in model parameterizations, can explain this relationship. Using a box model of ozone production and loss, we show that a negative ozone‐humidity slope is only achieved by the inclusion of VPD‐dependent dry deposition, suggesting that this mechanism may explain the observed ozone‐humidity correlation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it