Effects of age and experience on reproductive performance of captive red wolves (<i>Canis rufus</i>)
Bibliographic record
Abstract
Propagation programs contribute to the conservation of a species by preserving genetic and demographic stock that may be used to reinforce or re-establish wild populations. Identifying traits that affect reproductive success is essential to achieve this goal. Longitudinal reproductive events of the captive population of endangered red wolves (Canis rufus Audubon and Bachman, 1851) were investigated to determine whether parental age, breeding experience, and rearing type were factors in reproduction, litter size, and sex ratio, as well as viability of offspring. Younger wolves were more likely to reproduce and produce larger litters than were older individuals. The age of the female, but not the male, had a negative effect on pup survival. Wolves that had prior experience in offspring production were more likely to reproduce again than were individuals that had no prior reproductive success, but prior sexual experience alone was not a factor in offspring production. Parental breeding experience had a negative effect on pup survival, but no apparent relationships with litter size or sex ratio. Declines in reproduction, fitness, and survival with advancing age suggest the effect is due to senescence, the onset of which occurs at 8 years of age in females. The results are consistent with the breeding-experience hypothesis.
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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 | 0.002 |
| 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".