Variation in age of primiparity in mountain goats (<i>Oreamnos americanus</i>) estimated from horn growth increments
Bibliographic record
Abstract
Variation in age of primiparity is important for population dynamics and wildlife management because it can affect population growth. Using a novel technique based on the trade-off between annual horn growth and reproduction, we estimated the age of primiparity for 2274 female mountain goats (Oreamnos americanus (Blainville, 1816)) harvested across British Columbia, Canada, from 1976 to 2019. We then investigated spatio-temporal variation in the probability that harvested females were primiparous when aged 3, 4, or 5 years and older using Bayesian ordinal regressions. We found that the probability of primiparity at 3 years decreased over time in nearly all mountain ranges. In the Coastal Mountain range, however, the probability of primiparity at age 3 significantly increased. These results suggest that the large coastal populations of mountain goats could be more resilient to harvest than other populations in British Columbia, which may be experiencing environmental effects promoting later primiparity. Models predicting age of primiparity from annual growth measures are a valuable tool for wildlife management and could help conservation of many species.
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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 | 0.000 |
| 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".