The Use of Critical Levels for Determining Plant Response to Ozone in Europe and in North America
Bibliographic record
Abstract
Critical levels to determine plant response to ozone (O3) have been used in Europe since the 1980s, utilizing the concentration-based AOT40 to relate plant response to ambient O3 exposure. More recently, there has been progress in Europe toward utilizing flux-based critical levels, because plant response is more closely related to O3 uptake than to the amount of O3 in ambient air. Flux-based critical levels are plant species specific; data for parameterization of flux-based critical levels models are lacking for most plant species. Although flux-based critical levels are now being used for a limited number of agricultural crops and tree species where data are available, the use of flux-based critical levels is limited by the lack of adequate consideration and incorporation of plant internal detoxification mechanisms in flux modeling. Critical levels have not been used in North America; however, recent interest in the U.S. and Canada for using critical loads for nitrogen and sulfur has generated interest in using critical levels for O3. A major obstacle for utilization of critical levels in North America is that ambient air quality standards for O3 in the U.S. and Canada are concentration based. It appears that cumulative exposure-based metrics, particularly when implemented with a quantification of peak concentrations and environmental variables, such as a drought index, are currently the most useful to relate O3 to vegetation response. Because data are unavailable to quantify detoxification potential of vegetation, effective flux models are not available to determine plant response to O3.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".