The use of lichens as indicators of ambient air quality in Southern Ontario
Bibliographic record
Abstract
The inverse relationship between arboreal lichen species richness and sulphur dioxide in ambient air has been thoroughly documented in the literature. Previous work in southern Ontario has shown that lichen bioindication can identify areas of potential concern regarding air quality. The EMAN suite of l i chens was applied in the City of Samia by surveying 458 Sugar Maple trees, in order to test the applicability of lichen bioindication under conditions of high mean S02 levels and high species richness values. The results of the survey were explored using Geographic Information Systems. A spatial relationship between lichen community variables, the Bluewater Bridge and the highway was identified. Lichen species richness, lichen percent cover and Index of Atmospheric Purity values were higher along the bridge and highway. No strong gradients were found between other known pollution sources and no lichen deserts were identified. The most common community grouping consisted of Physcia millegrana Degel, Candelaria concolor (Dicks) B. Stein, Physcia aipolia (Ehrh ex Humb.) Furnrohr; all of which are known nitrophytes. The relationship between substrate pH and lichen species richness was examined. Sites with a known source of anthropogenic chemical contamination were found to have a correlation of l=0.8 between lichen species richness and pH. The inverse was found for sites with no known source of contamination with a correlation of r 2 =-0.72. The findings suggest that species richness may be influenced by altering substrate pH which promotes the growth of nitrophytic species capable of tolerating high S02 levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".