Research Note Environmental Contaminant Concentrations in Canada Goose (Branta canadensis) Muscle: Probabilistic Risk Assessment for Human Consumers
Bibliographic record
Abstract
The issue of food insecurity affects millions of people in the United States every year. Often these people rely on soup kitchens, food banks, and shelters for proper meals, and these organizations often depend on donations to meet needs. One of the most limited food resources is meat. To help alleviate this problem, the U.S. Department of Agriculture Wildlife Services donates more than 60 tons of wild game (deer, moose, feral hogs, goats, geese, and ducks) to a variety of charitable organizations each year. Although commercially produced meat routinely undergoes screening for contaminants, potential exposure to environmental contaminants from eating wild game is not well characterized. In this study, the concentration of 17 contaminants of concern in the breast meat of wild geese was examined. These concentrations were then used in a probabilistic model to estimate potential risk associated with consumption of this meat. Based on model predictions, more than 99 % of all adults were below exposure limits for all of the compounds tested. For all consumer age classes modeled, consumption of wild goose meat may expose a small fraction of these populations to levels of lead higher than the recommended exposure limits. Similarly, mercury exposure was predicted to be higher than the recommended limits when the meat was served as steaks. This information about concentrations of contaminants of concern in goose meat and potential exposures associated with meat consumption based on probabilistic models will enable others to make informed decisions about the risks associated with the consumption of wild meat. Environmental contaminants, either naturally occurring
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".