Acidification and recovery of aquatic ecosystems in south central Ontario, Canada: regional application of the MAGIC model
Bibliographic record
Abstract
Abstract. The dynamic model MAGIC was applied to 25 lakes in south-central Ontario, Canada using a regional modelling methodology. Soil and lake water chemistry for each lake catchment was simulated for the period 1850–2050. Sulphate (SO42–) deposition forecasts were derived from recently proposed emission reductions, which correspond roughly to a 50% reduction in SO42– deposition by 2010 from the 2000 baseline. Changes in SO42– deposition had a significant impact on lake chemistry. Simulated lake water chemistry showed a recovery potential under the current deposition scenario; by 2050 concentration levels recovered to values predicted for the early 1900s. Moreover, simulated future lake water chemistry showed significant recovery compared to 1975 levels. However, although regional simulations predict that base cation losses have decreased in recent years, soils in the region will continue to acidify with Ca2+ losses dominating depletion of the exchangeable pool. Base cation losses from the exchangeable pool are currently buffering lakes against the impacts of acid deposition; ultimately base cation inputs into the lakes will decrease as exchangeable base cation pools become depleted. Further emission reductions are necessary to ensure continued recovery from acidification. Keywords: regional dynamic model, sulphate, acid neutralising capacity, critical loads, lake chemistry, soil base saturation
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".