Forest health effects due to atmospheric deposition: Findings from long-term forest health monitoring in the Athabasca Oil Sands Region
Bibliographic record
Abstract
) with the potential for acidifying deposition and impacts to forest health. This article integrates the findings presented in the Oil Sands Forest Health Special Issue, which reports on the results of 20 years of forest health monitoring, and addresses the key questions asked by WBEA's Forest Health Monitoring (FHM) Program: 1) is there evidence of deposition affecting the environment?, 2) have there been changes in deposition or effects over time?, 3) do acid deposition levels require management intervention?, 4) what are major sources of deposited substances? and 5) how can the program be improved? Deposition of sulphur, nitrogen, base cations (BC), polycyclic aromatic compounds and trace elements decline exponentially with distance from sources. There is little evidence for acidification effects on forest soils or on understory plant communities or tree growth, but there is evidence of nitrogen accumulation in jack pine needles and fertilization effects on understory plant communities. Sulphur, BC and trace metal concentrations in lichens increased between 2008 and 2014. Source apportionment studies suggest fugitive dust in proximity to mining is a primary source of BC, trace element and organic compound deposition, and BC deposition may be neutralizing acidifying deposition. Sulphur accumulation in soils and nitrogen effects on vegetation may indicate early stages of acidification. Deposition estimates for sites close to emissions sources exceed proposed regulatory trigger levels, suggesting a detailed assessment of acidification risk close to the emission sources is warranted. However, there is no evidence of widespread acidification as suggested by recent modeling studies, likely due to high BC deposition. FHM Program evolution should include continued integration with modeling approaches, ongoing collection and assessment of monitoring data and testing for change over time, and addition of monitoring sites to fill gaps in regional coverage.
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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 | 0.001 |
| 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 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".