Horn growth rate and longevity: implications for natural and artificial selection in thinhorn sheep (<i>Ovis dalli</i>)
Bibliographic record
Abstract
We used horn measurements from natural and hunted mortalities of male thinhorn sheep Ovis dalli from Yukon Territory, Canada, to examine the relationship between rapid growth early in life and longevity. We found that rapid growth was associated with reduced longevity for sheep aged 5 years and older for both the hunted and natural mortality data sets. The negative relationship between growth rate and longevity in hunted sheep can at least partially be explained by morphologically biased hunting regulations. The same trend was evident from natural mortalities from populations that were not hunted or underwent very limited hunting, suggesting a naturally imposed mortality cost directly or indirectly associated with rapid growth. Age and growth rate were both positively associated with horn size at death for both data sets, however of the two growth rate appeared to be a better predictor. Large horn size can be achieved both by individuals that grow horns rapidly and by those that have greater longevity, and the trade-off between growth rate and longevity could limit horn size evolution in this species. The similarity in the relationship between growth rate and longevity for hunted and natural mortalities suggests that horn growth rate should not respond to artificial selection. Our study highlights the need for the existence and study of protected populations to properly assess the impacts of selective harvesting.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".