Mercury in the Lake Simcoe aquatic environment
Bibliographic record
Abstract
Since 1970 when mercury was first measured in fish and sediment from watercourses adjacent to industrial mercury sources (e.g. the St. Clair River - Lake St. Clair system of the Great Lakes and the English - Wabigoon River system of northwestern Ontario), the Province of Ontario has expanded its surveillance program to lakes and streams throughout Ontario. Popular angling species have been collected for mercury analysis from Lake Simcoe over the past few years. For the most part, fish from this lake are low in mercury (less than 0.5 parts per million) and suitable for consumption. Some of the larger predatory fish however, most notably walleye (yellow pickerel), do contain levels of mercury that make these fish suitable only for occasional consumption. The very large walleye (over 30 inches in length) are not recommended for consumption at all. No significant industrial source of mercury in the Lake Simcoe Basin has been identified, therefore, the cause of elevated levels in the large walleye could not be immediately identified. In order to better evaluate the mercury levels and possible sources in the basin, an extensive field survey program was implemented in the winter of 1977. Samples of fish, water and sediments were collected throughout the lake. Existing or past sources that could potentially contribute mercury (municipal discharges, agricultural drainage, sanitary landfill site runoff, etc.) were studied. The following report outlines the findings of the investigation and draws conclusions about the significance of mercury in fish from Lake Simcoe.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".