Methylmercury bioaccumulation in sport fish and the relation to human exposure
Bibliographic record
Abstract
A long-lasting contaminant, methylmercury (MeHg), builds up in human bodies over a lifetime. Regularly eating contaminated fish may accumulate mercury to an amount that raises health concerns, especially for children and babies. Methylmercury is found throughout the parts of the fish that are eaten; cleaning or cooking methods cannot effectively reduce mercury exposure. The main focus of this thesis concerns the fish contamination in Lake Ontario and to conserve health from eating contaminated sport fish. Although mercury (Hg) is tightly regulated, mercury levels in fish still gradually increase throughout their life spans. Through the field data provided by the Ontario Ministry of Environment (MOE), greater amounts of methylmercury are found in older fish and predatory fish that eat other fish as part of their diet. A bioenergetics computer program, called Generic Bioaccumulation Model (BGM) (Luk, 1996), simulating the bioaccumulation of Hg in fish was applied to provide a good estimation of mercury levels for different species. It is an excellent tool in predicting the trends and magnitude of mercury levels among six sport fish in Lake Ontario. In addition, an estimation of human mercury consumption from fish was also developed. In most of the fish species, there is minimal risk to humans when eating fish less than two times a week. The species Walleye (Stizostedion vitreum) and its quality and quantity are of greatest concern, since it exhibits the highest mercury level among the six sport fish species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".