Male harassment of female New Zealand sea lions, <i>Phocarctos hookeri</i>: mortality, injury, and harassment avoidance
Bibliographic record
Abstract
Sexual aggression by male pinnipeds during breeding can lead to female injury and death, affecting reproductive success, fecundity, and increasing the cost of mating for females. Thus, females that employ strategies to minimize the probability of being injured will be at an advantage. Here we investigate the extent of injuries and the number of deaths attributed to male harassment, and test the hypothesis of whether the arrival and departure behaviour of female New Zealand sea lions (Phocarctos hookeri (Gray, 1844); NZSL) at Sandy Bay, Enderby Island, the Auckland Islands, is adapted to reduce the chance of injury or death from encounters with male NZSLs. During the breeding season, harassment by non-territorial male NZSLs causes mortality in adult female NZSLs, approximately 5 in every 1000 females breeding each year. Permanent scars from male bites are observed on 84% of adult females. This mortality and visible injury rate only represents the direct impacts on female NZSL from male harassment. Indirect impacts, such as the time and energy cost of avoidance behaviour, pup separation, and pup injury and death, can have as significant long-term effects on individuals and the population. We find that male harassment can influence the behaviour of individuals in NZSL breeding harems.
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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) | 0.002 | 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".