Decreased Acid Deposition and the Chemical Recovery of Killarney, Ontario, Lakes
Bibliographic record
Abstract
Lakes in Killarney Park near Sudbury, Ontario, Canada, have shown dramatic water quality changes including general increases in pH and alkalinity, and decreases in SO4(2-), base cations and metals. While some lakes have recovered to pH > 6.0, many are still highly acidic despite decades of improvement. Very high historical S deposition related to emissions from the Sudbury metal smelters dominated the acidification process in this region. However, since the implementation of substantial S emission controls (90%) at the smelters, the Sudbury emissions are no longer the major source of S deposition in the Sudbury area. Wet deposition of SO4(2-) and SO4(2-) concentrations in lakewaters at Killarney now approach values in the Dorset, Ontario, area, about 200 km from Sudbury. This suggests that the S deposition to the Killarney area is now primarily from long-range transport, not from local sources. Studies of Killarney lakes are revealing the complex nature of the chemical recovery process. As lake acidity decreases, other changes including decreased Ca2+ concentrations, increased transparency, and altered thermal regimes may potentially affect some of these ecosystems. It is clear that continuing assessments of the recovery of Killarney lakes, within a multiple-stressor framework, are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".