Dietary Intakes and Plasma Organochlorine Contaminant Levels among Great Lakes Fish Eaters
Bibliographic record
Abstract
Nutritional intakes and contaminant burdens should be assessed jointly in individuals who are at high risk of environmental exposures to contaminants through food. In this study, the authors used shore surveys and community contacts to recruit 91 individuals who frequently consumed Great Lakes fish. These individuals provided dietary intake information and fasting blood samples for lipid and contaminant analyses. Participants ate an annual median of 88 meals of Great Lakes fish. Asian-Canadians consumed more total fish meals (i.e., Great Lakes, non-Great Lakes, and other) (medians = 213.0 females, 223.0 males) than Euro-Canadians (medians = 131.0 females, 137.5 males). The higher total fish consumption by Asian-Canadians was associated with a lower percentage of energy derived from fat, higher protein and iron intakes, and higher plasma concentrations of omega-3 essential fatty acids (e.g., median docosahexaenoic acid levels [microgram/l] in Asian-Canadian females = 5.48, males = 4.38; in Euro-Canadian females = 2.93, males = 2.27). Plasma organochlorine contaminant lipid weight concentrations varied by country of origin and by gender (e.g., median total polychlorinated biphenyls [microgram/kg] in Asian-Canadian females = 490.6, males = 729.0; in Euro-Canadian females = 339.6, males = 355.5). Age was the most consistent predictor (+ve) of contaminant concentrations, followed by years spent in Canada (for Asian-Canadians). Associations with sport fish consumption variables were less consistent than for the aforementioned predictors. Given both the health benefits and potential risks of fish consumption, policies that address diverse ethnocultural groups should support continued consumption of sport fish, but from less-contaminated sources than are currently used.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".