The role of phenotypic plasticity in responses of hunted thinhorn sheep ram horn growth to changing climate conditions
Bibliographic record
Abstract
When phenotypic change occurs over time in wildlife populations, it can be difficult to determine to what degree it is because of genetic effects or phenotypic plasticity. Here, we assess phenotypic changes over time in horn length and volume of thinhorn sheep (Ovis dalli) rams from Yukon Territory, Canada. We considered 42 years of horn growth from over 50,000 growth measurements in over 8000 individuals. We found that weather explained a large proportion of the annual fluctuation in horn growth, being particularly sensitive to spring weather. Only 2.5% of variance in horn length growth could be explained by an individual effect, and thus any genetic changes over the time period could only have had a small effect on phenotypes. Our findings allow insight into the capacity for horn morphology to react to selection pressures and demonstrate the overall importance of climate in determining growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".