Development and application of the FAB model to calculate critical loads of S and N for lakes in the Killarney Provincial Park (Ontario, Canada)
Bibliographic record
Abstract
In Sudbury, Canada, large reductions in sulfur emissions have resulted in reduced critical load exceedances and partial recovery of many lakes in the Killarney Provincial Park. The First-order Acidity Balance (FAB) model to calculate critical loads (CLs) for surface water includes the potential acidifying part of nitrogen, and takes into account the retention of nitrogen in both the terrestrial and aquatic part of the catchment. We have applied the FAB model to Killarney-lakes, and critical load functions for 43 lakes in the Park have been calculated. The model has been modified to include in-lake retention of nitrogen in upstream lakes in the calculation of CLs. This resulted in increased aquatic retention of nitrogen, giving higher CLs and thus making lakes in chains less sensitive to nitrogen deposition. Including upstream lakes in a simple manner increased the estimate of in-lake N retention by 47% on average and the lake-system method increased this estimate by 73% on average. Critical loads for nitrogen vary substantially within the Park, from 49 to 2472 meq m¯² yr¯¹. On the average N retained in the lake/sediment system (Nlake) was 57% of N deposited according to the modified model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".