Do critical load models adequately protect forests? A case study in south-central Ontario
Bibliographic record
Abstract
We calculated critical loads of acidity (S and S + N separately) for seven forested catchments in south-central Ontario, using a critical threshold designed to maintain the Ca/Al molar ratio above 1.0 or the base cation (BC; Ca + Mg + K) to Al molar ratio above 10 in soil solution. Critical loads are ~1050% lower using the BC/Al ratio compared with the Ca/Al ratio, and harvesting greatly increases forest sensitivity to acid deposition. If forests are harvested, critical load calculations indicate that further reductions in S and N bulk deposition are required to maintain the BC/Al ratio in soil solution above 10, but reductions in S deposition are only mandatory for three of the seven catchments. However, S export exceeds inputs in bulk deposition by 40100%. Our study indicates that setting the critical threshold of BC/Al at 10 may not maintain soil base saturation above 20%, and that N export is unpredictable at current deposition levels. We calculate that SO4 leaching (and therefore deposition) must be reduced by between 10 and 74% to maintain healthy, productive forests in catchments that are harvested. More reliable estimates of base cation removals during harvest, minimum Ca leaching losses from soils that can occur without affecting forest productivity, and critical limits for soil base saturation are needed to improve these critical load estimates.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".