An assessment of contaminants in bison (<i>Bison bison athabascae</i>) in the Peace-Athabasca region
Bibliographic record
Abstract
Oil production activities have remained contentious in Canada due to the risk of contaminant exposure and environmental impacts. However, despite recent advances in monitoring, there is a lack of information on contaminant exposure and its associated impacts for many species at risk. The threat from contaminants to wood bison ( Bison bison athabascae) in the Peace-Athabasca region, located principally in northeastern Alberta, is of particular concern, given the small size of the at-risk herds and the potential combined impacts of various stressors, including contaminants, disease, and climate change. Here, we review the available literature on contaminants in wood bison in the Peace-Athabasca region, extracting information on objectives, study design, location, contaminants, and analytic methods. We found six articles that assessed contaminants in wood bison and showed that, in the oil sands region, the species is exposed to a multitude of chemical contaminants. In particular, heavy metals, including arsenic, cadmium, lead, and inorganic mercury, were analyzed most often in bison kidney, liver, and muscle tissue. We also provide a comparison of the type and levels of heavy metals in wood bison and moose ( Alces alces). We found that articles on wood bison were dated relative to moose (i.e., mostly pre-1990s) and that fewer heavy metals and tissue types were assessed. Lastly, we discuss the gaps in knowledge on select heavy metals in these species and the known effects on human health. Overall, our results suggest that more research and monitoring are needed to understand the threats to wood bison, interacting and cumulative effects, and potential concerns related to human health and well-being for communities that rely on wood bison as a traditional food source.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".