Establishing Policy Relevant Background (PRB) Ozone Concentrations in the United States
Bibliographic record
Abstract
Policy Relevant Background (PRB) ozone concentrations are defined by the United States (U.S.) Environmental Protection Agency (EPA) as those concentrations that would occur in the U.S. in the absence of anthropogenic emissions in continental North America (i.e., the U.S, Canada, and Mexico). Estimates of PRB ozone have had an important role historically in the EPA's human health and welfare risk analyses used in establishing National Ambient Air Quality Standards (NAAQS). The margin of safety for the protection of public health in the ozone rulemaking process has been established from human health risks calculated based on PRB ozone estimates. Sensitivity analyses conducted by the EPA have illustrated that changing estimates of PRB ozone concentrations have a progressively greater impact on estimates of mortality risk as more stringent standards are considered. As defined by the EPA, PRB ozone is a model construct, but it is informed by measurements at relatively remote monitoring sites (RRMS). This review examines the current understanding of PRB ozone, based on both model predictions and measurements at RRMS, and provides recommendations for improving the definition and determination of PRB ozone.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".