Mountain sheep management using data versus opinions: A comment on Boyce and Krausman (2018)
Bibliographic record
Abstract
Sustainable wildlife management must consider the possible evolutionary effects of harvest schemes (Festa-Bianchet 2017).A recent Editor's Message in the Journal of Wildlife Management (Boyce and Krausman 2018) about the Special Section on mountain sheep management and 2 invited papers (Coulson et al. 2018, Heffelfinger 2018) question whether quota-free, phenotypebased selective hunting of bighorn (Ovis canadensis) males can lead to a measurable evolutionary change in horn size over a few generations.Responding to the Editor's Message, I argue that evidence of harvest-caused evolutionary changes in mountain sheep horns is strong, and worthy of consideration in management plans.Those evolutionary changes are brought about by very intense artificial selection against males with rapidly growing horns, a trait with a strong genetic component (Poissant et al. 2008).
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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 | 0.029 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.073 | 0.088 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".