EVALUATING RISK OF LEAD EXPOSURE IN SCAVENGING SPECIES LINKED TO BIG-GAME HUNTING IN SASKATCHEWAN, CANADA
Bibliographic record
Abstract
For free-ranging animals, chronic exposure to lead, a toxic heavy metal, can manifest in discrete physiological changes, alter behaviours, and contribute to increased mortality. In Canada, although lead ammunition has been banned for hunting waterfowl, carrion contaminated with lead fragments from bullets used in rifle-hunting remains a potential source of exposure for wildlife, particularly scavenging species. Addressing these risks requires clearer understanding of both the source (rifle-harvested remains) and the consumers (scavengers). The objective of my research was to evaluate the risk of lead exposure to scavenging wildlife in Saskatchewan, Canada, by investigating the detection and quantification of lead in hunted animal remains (Chapter 2) and assessing scavenger community assemblages at kill sites (Chapter 3). In Chapter 2, I compared advanced imaging techniques (Biomedical Imaging and Therapy) with traditional medical x-rays to identify limitations in detecting and quantifying lead fragments in undesired animal tissues, such as viscera and organs (i.e., offal), left in the field by hunters. I also conducted a coarse geospatial analysis using provincial hunter harvest data to determine regions in the province where scavengers may face elevated lead exposure risks. In Chapter 3, I examined scavenger species assemblages observed feeding on white-tailed deer (Odocoileus virginianus) kill sites and analyzed how environmental factors influenced community composition and species richness. My findings revealed that medical x-rays underestimated lead exposure risk due to their limited spatial resolution, missing small, bioavailable fragments and misrepresenting fragment size. The geospatial analysis showed that hunter harvest densities for white-tailed deer and mule deer varied significantly across wildlife management zones, potentially influencing exposure risks for scavengers by means of offal-derived lead. Scavenger community analyses showed that snow depth influenced community assemblage, while ecoregion and minimum daily temperature significantly predicted species richness at kill sites. This research advances the understanding of lead contamination from big-game hunting and highlights key factors that influence scavenger exposure. These findings offer valuable insights to guide future management strategies aimed at reducing or mitigating lead exposure risks across the landscape.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".