L'enneigement hivernal : un facteur de variation du recrutement chez l'isard (<i>Rupicapra pyrenaica pyrenaica</i>)
Bibliographic record
Abstract
To assess the relationships between winter climatic conditions and population dynamics of mountain ungulates, we monitored over the long term an unhunted isard (Rupicapra pyrenaica pyrenaica) population living in the Pyrénées National Park. We used general linear modelling to assess (i) the influence of the observation date (between November and April) of the transect sampled and of the year on population recruitment (proportion of females 2 years of age and older with kids at heel) and (ii) the impact of snow accumulation during winter (cumulated snow fall from November to April) on kid mortality. As expected, the mean number of kids per female decreased over the observation period. The mean number of kids per female showed marked among-year differences in initial recruitment (measured on 31 October) as well as in kid mortality (measured as the decrease in recruitment between 31 October and 31 March). This model accounted for 80% of the variability observed in the field data. Among-year differences in snow accumulation accounted for most (86%) of the yearly variation observed in kid mortality. Our study demonstrates that winter snow may severely affect juvenile survival, and thereby population dynamics of mountain ungulates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".