Factors affecting low resident male siring success in one-male groups of blue monkeys
Bibliographic record
Abstract
In species that live in one-male/multi-female groups, resident males have more access to females than do bachelor males and should have a within-group reproductive advantage. We used a genetic analysis of 13 microsatellite loci to assign paternity to 111 offspring born over 10 years in 8 groups of wild blue monkeys. Resident males sired a maximum of 61% of the offspring conceived in their groups, indicating that despite their greater access to females, residents lost a substantial number of offspring to outsiders. A resident was less likely to sire an offspring when multiple females were in conceptive estrus, suggesting that it is difficult to monitor many fertile females simultaneously. Moreover, multiple estrous females likely attract competitor males, whose presence also decreased the probability that a resident sired an offspring. The negative effect of intruders on resident siring success may occur because females prefer competitors or because an increase in the number of intruders increases the challenge of effective mate guarding by a resident, leading him to miss rare mating opportunities. Tenure length did not affect resident siring success. Identifying the factors affecting patterns of paternity within species will help us to better understand the considerable variation in resident male siring success that occurs in one-male groups.
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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".