Calcium declines in northeastern Ontario lakes
Bibliographic record
Abstract
Thousands of lakes in northeastern Ontario, Canada, have been acidified by sulphur deposition associated with emissions from the Sudbury area metal smelters. However, water quality improvements including increased pH and reduced sulphate concentrations have followed large reductions in Sudbury emissions that were implemented, beginning in the 1970s. Substantial decreases in Ca concentrations accompanied these other changes in lakewater chemistry. Monitoring of 38 lakes 20128 km from Sudbury showed declines in Ca concentrations, averaging 2.7 µeq·L1·year1, over the period 19811999. Declines were particularly apparent during the 1990s, averaging 3.8 µeq·L1·year1. Paleolimnological reconstructions of the long-term Ca patterns in six lakes suggest that general lakewater Ca declines occurred through much of the 20th century. Comparison of recent measured Ca concentrations in 16 lakes with diatom-inferred pre-industrial Ca concentrations indicates that overall decreases in Ca have been large, averaging 74.6 µeq·L1 or 46%. Long-term Ca patterns may reflect a combination of factors including climatic changes, forest harvesting activities, and leaching by acid deposition, the effects of which we can not separate. Calcium declines have biological implications that will need to be considered in the development of appropriate targets as these lakes continue to recover from acidification.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".