A global review of contaminants in True Geese
Bibliographic record
Abstract
True Geese (genera Anser and Branta) are an important food source and a valuable indicator species across their wide geographic ranges. We conducted a review of contaminants in True Geese that compiled research from 56 journal articles, five reports, and three theses published between 2000 and 2024. These studies suggested that geese are at relatively low risk of deleterious health consequences from current levels of exposure to most legacy pesticides and metal contaminants. The exception was lead from lead shot and industrial pollution, which was considered a contaminant of concern for several species, including Canada geese ( Branta canadensis), snow geese ( Anser caerulescens), and greylag geese ( Anser anser) in 11 countries. Most studies we reviewed focused on geese in the southern parts of their ranges. Arctic-breeding ranges were underrepresented, despite substantial harvest and consumption of geese in Arctic communities. Furthermore, while metals and legacy contaminants such as dichloro-diphenyl-trichloroethane (DDT) and polychlorinated biphenyls (PCBs) were well-quantified, few studies monitored contaminants of emerging concern (CECs), such as microplastics, plastic additives, or perfluorochemicals (PFCs). We found no studies on the cumulative effects of contaminants on goose health. To better understand the sources and fate of contaminants in True Geese, we recommend four directions for future research. (1) Quantify CECs in geese near suspected hotspots (e.g., wastewater treatment plants and airports). (2) Sample eggs for multi-year monitoring of persistent organic pollutants (POPs), breast feathers for nonlethal assessment of metals, and muscle and liver for evaluating human consumption risk. (3) Conduct multi-year monitoring of geese across their full annual cycle to characterize annual fluctuations in contaminant loads associated with migration, moulting, and egg-laying, and link these contaminants to geographic sources. (4) Establish participatory biomonitoring networks in Arctic communities to fill geographical gaps and inform human health discussions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".