Territory quality of male sea otters in Prince William Sound, Alaska: relation to body and territory maintenance behaviors
Bibliographic record
Abstract
Based on optimality models, lekking males holding higher quality territories should spend more effort on territory maintenance and less effort on body maintenance. We tested the hypothesis that benefits are correlated with costs for male sea otters, Enhydra lutris (L., 1758). Activity state was recorded during focal follows of 10 individuals (n = 127). Higher quality territories had larger area, more food resources attractive to females, a higher ratio of protective shoreline edge, and higher accessibility for females evading male harassment. Contrary to our prediction, territory quality was uncorrelated with measures of cost: territory maintenance (patrolling, interacting) and body maintenance (feeding, grooming). We rejected the hypothesis that proximate benefits would be correlated with costs and suggested the following alternative working hypotheses: (i) given the high metabolic rate of sea otters, male breeding success may depend as much on maintaining body condition as maintaining a territory; (ii) higher quality territories with shoreline edge may not require additional patrolling effort; (iii) males may not expend extra effort in territory maintenance until more females come into estrus; or (iv) our seasonal measures of the benefits and costs of territoriality may not have accurately reflected factors influencing the switch between territorial and non-territorial tactics.
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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".