Effects of early horn growth on reproduction and hunting mortality in female chamois
Bibliographic record
Abstract
1. Environmental conditions during early development can affect the growth patterns of vertebrates, influencing future survival and reproduction. In long-lived mammals, females that experience poor environmental conditions early in life may delay primiparity. In female bovids, annual horn growth increments may provide a record of age-specific reproduction and body growth. Horn length, however, may also be a criterion used by hunters in selecting animals to harvest, possibly leading to artificial selection. 2. We studied three populations of chamois (Rupicapra rupicapra) in the western Alps to explore the relationships between female horn length and early growth, age of primiparity and age-specific reproduction. We also compared the risk of harvest to reproductive status and horn length. 3. Early horn growth was positively correlated with body mass in pre-reproductive females and with reproduction in very young and senescent adults. Females with strong early horn growth attained primiparity at an earlier age than those with weak early growth. Horn length did not affect hunter selection, but we found a strong hunter preference for nonlactating females. 4. Our research highlights the persistent effects of early development on reproductive performance in mammals. Moderate sport harvests are unlikely to affect the evolution of phenotypic traits and reproductive strategies in female chamois. A policy of penalizing hunters that harvest lactating females, however, may increase the harvest of 2-year-old females, which have high reproductive potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".