Assessment of changes in lake pH in southeastern Canada arising from present levels and expected reductions in acidic deposition
Bibliographic record
Abstract
An integrated acid rain assessment model was used to estimate pH for six clusters of lakes in southeastern Canada and scenarios of sulphate deposition that reflect the situation (a) before implementation of the SO2emission controls required by the Canada/U.S. Air Quality Agreement, (b) after implementation of Canadian controls, and (c) after implementation of Canadian and U.S. controls. Modelled lake pHs were always less than their estimated original values. To assess the ecological significance of the pH reduction, scenario "damage" was quantified as the percentage of cluster lakes having pH < 6, a threshold criterion sufficient to protect most aquatic biota. Care was taken to account for naturally acidified lakes. The integrated acid rain assessment model predicted that Canadian SO2controls will reduce damage in Ontario and Quebec but have little effect in Atlantic Canada. Implementation of U.S. SO2controls will further reduce damage throughout all regions, although it is conservatively estimated that from 5 to 24% of the lakes will still have pH < 6 depending on cluster. Extrapolating to the inventory of acid-sensitive lakes in southeastern Canada suggests that ~76 000 lakes and ~970 000 ha of lake area will remain chemically damaged unless additional reductions in SO2emissions are implemented beyond those required by the Air Quality Agreement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".