Effects of hunting pressure and timing of harvest on bighorn sheep (<i>Ovis canadensis</i>) horn size
Bibliographic record
Abstract
Trophy hunting can affect weapon size of wild animals through both demographic and evolutionary changes. In bighorn sheep ( Ovis canadensis Shaw, 1804), intense harvest of young males with fast-growing horns may have partly driven long-term decreases in horn size. These selective effects could be dampened if migrants from protected areas, not subject to artificial selection, survived and reproduced within hunted populations. Bighorn rams undertake long-distance breeding migrations in the weeks preceding the late-November rut. We analysed records of >7800 trophy bighorn rams shot from 1974 to 2019 in Alberta, Canada, to test the hypothesis that high harvest pressure during breeding migrations was correlated with a greater decrease in horn size. We compared areas with and without a pronounced harvest peak in late October, when male breeding migrations begin. Areas without a pronounced harvest peak in late October, which likely experienced a lower harvest rate, showed a similar temporal decline in horn size, but no increase in age at harvest suggesting a possibly weaker decline in horn growth. Our study suggests that unselected immigrants from protected areas could partly buffer the effects of intense trophy hunting only if harvest pressure was reduced when breeding migrations commence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".