Dynamic Modelling of Recovery from Acidification of Lakes in Killarney Park, Ontario, Canada
Bibliographic record
Abstract
During much of the 1900s, the lakes in Killarney Provincial Park have been exposed to high levels of acid deposition due to sulfur emissions from the nearby metal smelters in Sudbury. The sulfur emissions from this large point source have decreased to about 10% of what they were in the 1960s. Lake water quality in Killarney Park has greatly changed in response to reduced emissions, with noticeable declines in sulfate, aluminum and calcium concentrations. Here we apply the dynamic acidification model MAGIC to 3 lakes in Killarney Park. The lakes, which have different buffering capacities and response times, were selected to represent fast, intermediate and slow recovery from acidification. The model was calibrated to match observed data for the lakes and 4 different forecast scenarios for future sulfur deposition reductions were applied. The results indicate that there is still a large potential for improvement in the water quality in Killarney. The recovery time for the different lakes varies greatly. For the lake having the slowest response time several decades are needed for the chemistry to stabilize after implementation of deposition reductions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".