Mating tactic is associated with body condition loss in Rocky Mountain bighorn sheep rams ( <i>Ovis canadensis</i> )
Bibliographic record
Abstract
In polygynous mating systems, males often employ alternative mating tactics to enhance reproductive success. In Rocky Mountain bighorn sheep ( Ovis canadensis Shaw, 1804), the primary tactics are blocking, coursing, which involves mating chases, and tending, which involves mate guarding. While all three tactics can be energetically costly and diminish body condition, it remains unclear whether the associated costs differ between tactics and to what extent. We investigated the impact of mating tactics, specifically the proportion of the rut allocated to each, on body condition loss during the mating season in bighorn sheep rams. Using a non-invasive photographic method to estimate body condition loss, we found that the proportion of the rut a male spent tending significantly increased body condition loss. In contrast, the percentage of time spent blocking or coursing did not show a significant effect. Age was associated with the choice of mating tactic, with younger males predominantly coursing, older males primarily tending, and some intermediate-aged males employing both tactics concurrently. Blocking was rarely used. Despite the higher energetic costs, our results reveal the flexibility in tactic usage and indicate that tending, while demanding, is likely a high-cost, high-gain strategy, as tending rams are known to sire more offspring.
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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 | 0.000 |
| 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".