www.elsevier.com/locate/envpol Air quality in natural areas: Interface between the public, science and regulation
Bibliographic record
Abstract
Appropriate endpoints and exposureeresponse indices can improve assessment of air pollutant risk to forests in natural areas. Natural areas are important interfaces between air quality, the public, science and regulation. In the United States and Canada, national parks received over 315 million visits during 2004. Many natural areas have been experiencing decreased visibility, increased ozone (O3) levels and elevated nitrogen deposition. Ozone is the most pervasive air pollutant in North American natural areas. There is an extensive scientific literature on O 3 exposureetree response in chambered environments and, lately, free-air exposure systems. Yet, less is known about O 3 impacts on natural terrestrial ecosystems. To advance scientifically defensible O3 risk assessment for natural forest areas, species-level measurement endpoints must be socially, economically and ecologically relevant. Exposure-based indices, based on appropriate final endpoints, present an underused opportunity to meet this need. Exposureeplant indices should have a high degree of statistical significance, have high goodness of fit, be biologically plausible and include confidence intervals to define uncertainty. They must be supported by exposureeresponse functions and be easy to use within an air quality regulation context. Ozone exposureeresponse indices developed within an ambient air context have great potential for improving risk assessment in natural forest areas and enhancing scientific literacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.029 |
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".