Projecting Fish Mercury Levels in the Province of Ontario, Canada and the Implications for Fish and Human Health
Bibliographic record
Abstract
Fish mercury levels appear to be increasing in Ontario, Canada, which covers a wide geographical area and contains about 250 000 lakes including a share of the North American Great Lakes. Here we project 2050 mercury levels in Ontario fish, using the recently measured levels and rates of changes observed during the last 15 years, and present potential implications for fish and human health. Percentage of northern Ontario waterbodies where sublethal effects of mercury on fish can occur may increase by 2050 from 60% to >98% for Walleye (WE), 44% to 59-70% for Northern Pike (NP), and 70% to 76-92% for Lake Trout (LT). Ontario waterbodies with unrestricted fish consumption advisories for the general population may deteriorate from 24-76% to <1-33% for WE, 40-95% to 1-93% for NP, and 39-89% to 18-86% for LT. Similarly, Ontario waterbodies with do not eat advisories for the sensitive population may increase from 32-84% to 73-100% for WE, 9-72% to 12-100% for NP, and 19-71% to 24-89% for LT. Risk to health of Ontario fish and humans consuming these fish may increase substantially over the next few decades if the increasing mercury trend continues and updated advisories based on continued monitoring are not issued/followed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".