Air pollution, climate, soil acidity, and indicators of forest health in Ontario’s sugar maple forests
Bibliographic record
Abstract
Ontario’s hardwood forests are currently subjected to high levels of air pollution, but critical levels at which point adverse effects may occur are poorly known. In this study, we sampled 35 hardwood plots dominated by sugar maple ( Acer saccharum Marsh.) located along a contrasting climate, soil acidity, and air pollution gradient in southern Ontario to explore relationships between these potential ecosystem stressors and ecosystem responses. Foliar sulfur (S) and nitrogen (N) contents were positively correlated with modeled deposition, and foliose lichen species richness was negatively correlated with modeled air pollution levels (S deposition, N deposition, and atmospheric ozone AOT40), whereas foliar calcium, magnesium, and manganese contents were correlated with A-horizon soil acidity. Forest floor S and N contents and C/N ratios were related to soil pH, with high S and N contents and low C/N ratios occurring on the more acidic soil in the northern part of the region, which receives the lowest modeled loadings of S and N deposition and experience colder and wetter climate. Forest health as determined by canopy condition was not related to indices of air pollution, climate, or soil acidity, and no relationship was found among air pollution, soil acidity, and ground vegetation species richness or diversity.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".