Getting ready for the winter: Timing and determinants of molt in an alpine ungulate
Bibliographic record
Abstract
Because growth of new hairs entails energetic costs, individual condition and access to food should determine the timing of molt. Previous studies on the timing of molt in ungulates have mostly focused on the influence of age class and reproductive status, but the effects of body condition and environmental phenology have not been evaluated. Our goal was to assess how intrinsic traits and environmental conditions determine the timing of winter coat shedding in a mountain goat population monitored for 27 years. The date of molt completion followed a U shape with age, suggesting that senescence occurs in terms of the molting process in mountain goats. Juveniles of both sexes delayed molting in a similar fashion, but molt timing differed between sexes during adulthood. Males molted progressively earlier until reaching age when reproduction peaked, after which they started delaying molting again. Females reached earliest molt dates at age of first reproduction and then progressively delayed molt date. Lactating females molted 10 days later than barren females on average, but this only occurred in females in good condition. Thus, although it has been shown that reproduction delays molt in ungulates, our results indicate that body condition can override this effect. Overall, our results revealed that access to both extrinsic and intrinsic resources is one of the key mechanisms driving molting processes in a mammalian herbivore.
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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 | 0.000 |
| 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".