The Influence of Vehicular Air Pollution on Lichen Abundance in Two Central Ontario Forests
Bibliographic record
Abstract
Lichens have been used extensively as a bioindicator to test for damage by vehicular air pollution in urban and rural areas; however, the degree of influence vehicular air pollution has on lichen in forest ecosystems is not as well understood. The influence of vehicular emissions on two forests in southern Ontario was assessed by studying changes in lichen species composition and density on trees. The sites included in this study were Indian Point Provincial Park and Mark S. Burnham Provincial Park, located next to low use and high use roads respectively to compare effects of point source vehicular emissions. Horizontal transects, parallel with the road, were established at predetermined distances from the edge of the forest. All Sugar Maple trees with a diameter at breast height (DBH) of 10cm or greater, and which were within 2.5m of each transect were sampled for percent cover of microlichen and macrolichen. It was expected that the percent cover of lichens would vary between the sites and the distances from the road at which they were located. The study found a significantly greater percent coverage of macrolichens at Indian Point than at Burnham (p<0.001, t=-5.481), but significantly less microlichens (p=0.001, t=3.470). There was a decrease in macrolichen coverage as distance from the road increased at Indian Point, unlike Burnham which exhibited no change. A positive relationship between microlichen coverage and distance from the road was found in both sites. These findings suggest that, up to and possibly over 100m into the forest, the Burnham Park site is in a constant state of disturbance due to anthropogenic air pollution activities in the surrounding area. However, further studies investigating the role of a natural forest edges on lichen tree coverage should be conducted before concrete conclusions can be made on the influence of air pollution on lichen populations in forests.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".