Effect of cooking temperature on metal concentrations and speciation in fish muscle and seal liver
Bibliographic record
Abstract
Fish and marine mammals constitute a significant part of the country food diet of many Indigenous communities in Canada. These animals sometimes accumulate essential elements as well as elevated levels of toxic metals. We experimentally assessed how changes in cooking temperature (23-99 °C by boiling) modified elemental concentrations in whitefish muscle and grey seal liver (two organs commonly consumed in some northern communities). Wet and dry elemental concentrations changed linearly as a function of temperature, and two patterns were observed: methylmercury, selenium, and rare earth elements tended to remain associated with the food during cooking, whereas alkali, alkaline-earth metals, and arsenic were significantly transferred to cooking juices. Mass balances indicated that speciation of mercury was stable during cooking. Because elements generally behaved similarly as those of their periodic table group or their ecotoxicological classes (A, B, intermediate), we propose that elemental behavior during cooking is partly a function of chemical affinity, and this relationship can be used to predict the behavior of data-poor elements of emerging concern, such as technology-critical elements. Furthermore, the marked increases and decreases in elemental concentrations during cooking (e.g., -14% As and +39% Se in whitefish; -22% Cd and +55% Hg in seal liver, on a wet weight basis) should be considered when assessing risk because current exposure models usually only consider elemental concentrations in raw food.
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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 | 0.000 | 0.000 |
| 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".