Using reintroduction to elucidate breeding system, breeding phenology, and infanticide for a solitary carnivore (fisher, <i>Pekania</i> <i>pennanti</i>)
Bibliographic record
Abstract
Breeding systems affect the timing of reproduction, spacing patterns and social organization, individual fitnesses, and population sizes. For many species, information on breeding systems and mating is limited or untested in wild populations, resulting in management actions that are incompletely informed. We used photographic data collected on a reintroduced fisher (Pekania pennanti (Erxleben, 1777)) population in northern California, USA, to test hypotheses about the breeding system, the timing of breeding, and the potential for male infanticide. We documented fishers of both sexes breeding with multiple partners in the same year, demonstrating polygynandry. We use logistic and linear regression to evaluate the timing and frequency of male visitation at 262 reproductive dens used by 50 individual females. Of 46 documented copulations and 577 male visits, 100% and 95%, respectively, occurred while females had kits in their natal dens. Seventy-five percent of documented male visits occurred before 31 March and 95% occurred before 17 April. Observed breeding occurred within 3.1 ± 1.6 days (mean ± SD) of locating females’ natal dens. We found no evidence for male-directed infanticide. Our results add precision to the timing of the reproductive cycle and provide the first descriptions of male–female interactions for wild fishers.
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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 | 0.001 | 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".