Regulations and hunter preference affect mountain goat harvest and horn length
Bibliographic record
Abstract
Wildlife management attempts to balance consumptive and non‐consumptive values to manage hunting opportunities, considering population resilience. Mountain goats Oreamnos americanus are particularly sensitive to harvest. Using data from 33 792 mountain goats harvested in British Columbia (BC), Canada, between 1977 and 2019, we performed Bayesian regressions to examine the effect of regulations (limited entry hunting (LEH) or general open season (GOS)) on yearly harvest and harvest sex ratios. We also investigated temporal trends and the effect of licensed hunter residency (resident in British Columbia, or not) on harvest sex ratios. We then examined how horn length of harvested mountain goats was influenced by sex, year of harvest, age and mountain range. The more restrictive LEH regulations generally reduced harvest of mountain goats. The annual proportion of males harvested appeared independent of regulation and increased over time. Non‐resident hunters harvested a greater proportion of males compared to resident hunters. The combined length of the first and second horn growth increments decreased slightly with age at harvest for males but increased for females, suggesting a possible very weak hunter selection for males with rapid early horn growth and possibly against lactating females. Our study supports LEH regulations and hunter education to distinguish sex and age as key tools for mountain goat harvest management. Similar tools could be considered to manage other ungulates that are sensitive to harvest.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".