Modelling Human Exposure of Methylmercury from Fish Consumption
Bibliographic record
Abstract
Abstract Mercury and its compounds are widely distributed in the environment and the principal cause of methylmercury accumulation in humans is fish consumption. The rate of methylmercury accumulation depends on many factors including the amount, size, type and frequency of fish consumed, as well as contamination levels in the aquatic habitat. The ability to predict accurately human exposure to methylmercury through fish consumption is essential to the setting of public consumption guidelines. This paper describes the development of an innovative method of estimating human exposure to methylmercury through sport fish consumption by mathematical modelling. Through a judicious combination of fish methylmercury bioaccumulation models and survey information on human fish-eating habits, the model allows for a scientifically based estimation of the average daily exposure to methylmercury from fish consumption. It provides a practical tool to estimate the methylmercury uptake from a fish diet as governed by the diet frequency, fish species and fish size. The efficacy of the model is demonstrated by application to six common Lake Ontario fish species. Results showed that the human methylmercury exposure from fish consumption is a serious issue, as demonstrated by the exceedance of the tolerable daily intake levels in many instances. It was also found that the level of human methylmercury uptake depends heavily on the species of fish consumed; among the six species studied, walleye carries the highest risk, followed by yellow perch, while rainbow trout seems to be the safest with the lowest bioaccumulation levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 teacher head, 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".