Canadian Experiences in Development of Critical Loads for Sulphur and Nitrogen
Bibliographic record
Abstract
Critical loads are a broad-scale modelling approach designed to assess the potential risk of pollutants to ecosystems. A description of the methodology for estimating critical loads (sulphur and nitrogen) for acid deposition (CL(A)) for upland forests in eastern Canada is presented, using a case study in central Ontario. In eastern Canada, CL(A) have been calculated for upland forests, with the objective of maintaining the molar ratio of base cations to aluminium in soil solution above 10. In the current approach, nitrogen (N) dynamics including N fixation, N immobilisation and denitrification have been set to zero. Further, critical load estimates presented in this study do not include nutrient removals through harvesting, and dry deposition input is estimated to be 20 percent of wet (1994 to 1998) deposition. Critical loads were calculated separately for Ontario, Quebec and the Maritime Provinces (New Brunswick, Nova Scotia and Newfoundland) using the same methods, but using different soil and forest databases. Mean area-weighted critical loads among provinces are similar, ranging between 273 eq ha--1 yr--1 (Newfoundland) and 512 eq ha--1 yr--1 (Ontario). Preliminary estimates indicate that more than 50 percent of the upland forest area in Ontario and Quebec and between 10 (Newfoundland) and 33 percent (Nova Scotia) of upland forest in the Maritimes receive acid deposition in excess of the critical load. Current efforts are being directed toward improving the accuracy of critical load estimates and current exceedances using better estimates of dry deposition and harvesting removals, and investigating the linkage between exceedance of the critical load and adverse biological effects.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".