Water quality changes in south-central Ontario lakes and the role of local factors in regulating lake response to regional stressors
Bibliographic record
Abstract
The long-term monitoring of eight lakes near Dorset, Ontario, indicates that the water quality has changed significantly over the last 30 years. However, trends in the Dorset lakes may not be representative of changes in other south-central Ontario lakes, as the Dorset lakes are within a small area and span a limited gradient of lake and watershed characteristics. To determine the regional extent of water quality changes, we assessed the chemical changes in 36 diverse lakes that were first sampled between 1981 and 1990 and were resampled in 2004–2005. Similar to trends in the Dorset lakes, changes in the regional lakes included decreasing acidity, calcium, conductivity, metals, and phosphorus, and increasing dissolved organic carbon, nitrogen, sodium, and chloride. Water quality changes were driven by regional stressors, including acidic deposition, climate, and lakeshore residential development. However, stressor-induced responses differed among lakes. Increases in sodium and chloride were greater in developed lakes that were close to winter-maintained roads. Site-specific characteristics, such as lake and watershed morphometry, could not explain heterogeneous changes in the remaining water quality parameters. These results indicate that other factors play an important role in regulating individual lake response to regional stressors.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".