Plural breeding in Gray Wolf (<i>Canis lupus</i>) packs: how often?
Bibliographic record
Abstract
The occurrence of more than a single female breeder in North American Gray Wolf (Canis lupus) packs, i.e., plural breeding, is well known, but its incidence has not been estimated since 1982. Using winter pack size as an index to plural breeding inwolves, I reviewed the literature from North American populations least exploited by humans to assess the general incidence of plural breeding. Generally winter packs >15 were associated with incidents of plural breeding. Wolf packs preying primarily on White-tailed Deer (Odocoileus virginianus) and in locations south of 52°N latitude seldom exceeded 10–15. Plural breeding occurred in packs preying primarily on larger ungulates in areas mostly above 52°N. The estimated incidence of plural breeding in the overall wolf population was <15% and perhaps <10%, which is lower than a 1982 estimate of at least 20–40%. I discuss reasons why plural breeding is associated with larger prey.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".