Improvements in fish polychlorinated biphenyl and other contaminant levels in response to remedial actions in Hamilton Harbour, Ontario, Canada
Bibliographic record
Abstract
Hamilton Harbour, located in Ontario, Canada at the western end of Lake Ontario, is recognized as one of the most anthropogenically-impacted regions within the Great Lakes and is currently listed as an Area of Concern. One of the Beneficial Use Impairments for the harbour has been restrictions on fish consumption due to elevated contaminant levels. In this study, we examined past and recent fish contaminant data collected by the Ontario Ministry of the Environment and Climate Change in partnership with other agencies to evaluate temporal trends in fish contaminant concentrations. Measurements for both resident and migratory sport fish as well as juvenile/forage fish were considered, with analysis focused on polychlorinated biphenyls, the group of chemicals identified as the major contaminant of concern. Current contaminant levels were evaluated against fish consumption advisory benchmarks used by Ontario Ministry of the Environment and Climate Change, and compared with corresponding observations for other locations across the Great Lakes, including other Areas of Concern. The results show statistically significant improvements in fish contaminant levels within Hamilton Harbour, with recent fish mercury concentrations below the first advisory benchmarks for all species included in this study. Polychlorinated biphenyl concentrations declined by 59–82% from historical levels, although this decline was not statistically significant in Brown Trout, Common Carp, Freshwater Drum and White Sucker. Further, all species exhibit recent polychlorinated biphenyl concentrations above the first consumption advisory benchmark of 105 ng g−1. Compared to other Great Lakes locations, including other areas of concern, Hamilton Harbour polychlorinated biphenyl concentrations remain amongst the highest. The results suggest that recovery of Hamilton Harbour is still on-going.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".