Timing of breeding and reproductive performance of female European rabbits in response to winter temperature and body mass
Bibliographic record
Abstract
Environmental conditions frequently affect the reproduction of many mammal and bird species by modulating maternal body condition. In our long-term study of European rabbits, Oryctolagus cuniculus (L., 1758), we investigated the effects of winter weather conditions and body mass on reproductive performance and reproductive timing. Specifically, we tested whether winter temperature affects both reproductive timing and reproductive performance of female rabbits or whether females compensate for low body mass after winter by a shift in the timing of the onset of breeding. Winter body mass loss was higher in years with lower winter temperatures, and the onset of breeding was delayed after these conditions. However, mean size and mass of the first litter of each season and seasonal fecundity were not reduced after harsh winters. At the individual level, females with lower winter body mass started to reproduce later, but we did not find any effects on litter size and mass. In contrast, breeding body mass was a strong predictor of the females' reproductive performance. We also found high between-year variation in mean body mass in late winter but not in mean breeding body mass. In conclusion, our results suggest that the negative effects of winter weather on the body mass of females, which should potentially affect their reproductive performance, were compensated for by delaying the onset of breeding.
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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 | 0.001 |
| 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) | 0.001 | 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".