Sampling Ozone Exposure of Canadian Forests at Different Scales: Some Case Studies
Bibliographic record
Abstract
The use of passive samplers in extensive monitoring, such as that used in national forest health monitoring plots, indicates that these devices are able to determine both spatial and temporal differences in ozone exposure of the plots. This allows for categorisation of the plots and the potential for cause-effect analysis of certain forest health responses. Forest exposure along a gradient of air pollution deposition demonstrates large variation in accumulated exposures. The efficacy of using passive samplers for in situ monitoring of forest canopy exposure was also demonstrated. The sampler data produced weak relationships with ozone values from the nearest "continuous" monitor, even though data from colocated samplers showed strong relationships. This spatial variation and the apparent effect of elevation on ozone exposure demonstrate the importance of topography and tree canopy characteristics in plant exposure on a regional scale. In addition, passive sampling may identify the effects of local pollutant gases, such as NO, which may scavenge ozone locally only to increase the production of this secondary pollutant downwind, as atmospheric reactions redress the equilibrium between concentrations of this precursor and those of the generated ozone. The use of passive samplers at the stand level is able to resolve vertical profiles within the stand and edge effects that are important in exposure of understorey and ground flora. Recent case studies using passive samplers to determine forest exposure to ozone indicate a great potential for the development of spatial models on a regional, landscape, and stand level scale.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".