Highly Competitive Reindeer Males Control Female Behavior during the Rut
Bibliographic record
Abstract
During the rut, female ungulates move among harems or territories, either to sample mates or to avoid harassment. Females may be herded by a male, may stay with a preferred male, or aggregate near a dominant male to avoid harassment from other males. In fission-fusion group dynamics, female movement is best described by the group's fission probability, instead of inter-harem movement. In this study, we tested whether male herding ability, female mate choice or harassment avoidance influence fission probability. We recorded group dynamics in a herd of reindeer Rangifer tarandus equipped with GPS collars with activity sensors. We found no evidence that the harassment level in the group affected fission probability, or that females sought high rank (i.e. highly competitive and hence successful) males. However, the behavior of high ranked males decreased fission probability. Male herding activity was synchronous with the decrease of fission probability observed during the rut. We concluded that male herding behavior stabilized groups, thereby increasing average group size and consequently the opportunity for sexual selection.
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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 | 0.000 | 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) | 0.002 | 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".