Variation in reproductive success of male and female Columbian ground squirrels (<i>Urocitellus columbianus</i>)
Bibliographic record
Abstract
The imbalanced reproductive success of polygynous mammals results in sexual selection on male traits like body size. Males and females might have more balanced reproductive success under polygynandry, where both sexes mate multiply. Using 4 years of microsatellite DNA analyses of paternity and known maternity, we investigated variation in reproductive success of Columbian ground squirrels, Urocitellus columbianus (Ord, 1815); a species with multiple mating by both sexes and multiple paternity of litters. We asked whether male reproductive success was more variable than that of females under this mating system. The overall percentage of confirmed paternity was 61.4% of 339 offspring. The mean rate of multiple paternity in litters with known fathers was 72.4% (n = 29 litters). Estimated mean reproductive success of males (10.27 offspring) was about thrice that of females (3.11 offspring). Even after this difference was taken into account statistically, males were about three times as variable in reproductive success as females (coefficients of variation = 77.84% and 26.74%, respectively). The Bateman gradient (regression slope of offspring production on number of successful mates) was significantly greater for males (βM = 1.44) than females (βF = 0.28). Thus, under a polygynandrous mating system, males exhibited greater variation in reproductive success than females.
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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".