Relative effects of vehicle pollution, moisture and colonization sources on urban lichens
Bibliographic record
Abstract
Summary Lichens are sensitive to pollution and have therefore been used as air quality indicators. While the distribution of lichens within cities may reflect the distribution of traffic pollution, other factors affecting urban lichens are correlated with this pollution – particularly moisture and the presence of lichen colonization sources. Our objective was to determine the relative importance of these three factors – vehicle pollution, moisture and colonization sources – in determining lichen distribution within a city. We surveyed macrolichen richness and cover on 420 trees within 84 sites across urban O ttawa, C anada. The sites were selected to minimize correlations among vehicle pollution, moisture and colonization sources. Model‐averaged standardized regression coefficients were used to compare the relative effects of these three variables on macrolichen richness and cover. We found that macrolichen cover was most strongly related to vehicle pollution and that this effect occurred within 300 m of a site. However, macrolichen species richness responded more strongly to both moisture (at the site centre) and colonization sources (within 1000 m of the site) than to vehicle pollution. Synthesis and applications . Our results suggest that macrolichen cover, but not macrolichen species richness, can be used as an indicator of urban traffic pollution. Our results also suggest that, to promote urban macrolichen diversity, it would be more effective to increase the availability of lichen colonization sources (nearby trees) than to control traffic pollution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".