Using reproductive data to model American black bear cub orphaning in Manitoba due to spring harvest of females
Bibliographic record
Abstract
Abstract Animal rights groups have lobbied for the cancellation of Manitoba's spring hunting season for American black bear (Ursus americanus), contending that hundreds of cubs are orphaned each year. We developed a mathematical model to estimate the number of black bear cubs that may be orphaned in Manitoba because of the spring hunting season. The model used information from annual questionnaires mailed to resident hunters, Outfitter Declaration Forms from operators who provide services to non-resident clients, and analysis of reproductive tracts (>200 for both spring and fall seasons) and tooth samples (>1,100). To accurately reflect the number of cubs orphaned each spring, the model accounted for cub losses (both litter reduction and total litter loss) prior to a female being harvested using values from the literature. Although the data was not used in the model, evidence from the examination of reproductive tracts suggests that total litter loss of hunter killed bears can be determined by examining the condition of the uterus and ovaries. The model estimated that on average, 41 cubs were orphaned for each of the spring seasons between 1996 and 2000. This number represents <2% of the estimated number of cubs that may die annually in Manitoba from natural causes.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".