Disentangling Natural and Anthropogenic Forms of Mortality and Serious Injury in a Poorly Studied Pelagic Dolphin
Bibliographic record
Abstract
Killer whale ( Orcinus orca ) populations specialize in both prey and prey acquisition tactics around the world and may be a primary evolutionary driver of the habits of small cetaceans. Entanglement in fishing gear is the most significant anthropogenic threat to the survival of cetaceans worldwide. Distinguishing between natural and human-caused sources of mortality and injury is a key task in marine mammal conservation and management. In British Columbia (BC), Canada, mammal-eating killer whales co-occur with Pacific white-sided dolphins ( Lagenorhynchus obliquidens ). Bycatch mortality rates are unknown here due to lack of systematic fisheries observer coverage. Drawing from more than three decades of first-hand observations of killer whale attacks on Pacific white-sided dolphins, we identify common themes with respect to predatory behavior of killer whales and anti-predatory responses of dolphins. With input from veterinary pathologists, we outline clues to distinguish killer whale rake marks from scars and wounds likely to be caused by fishery interactions. We examined photographs of 415 well-marked Pacific white-side dolphins for evidence of injuries and scars consistent with either killer whale attacks or fishery interactions. In this case study, healed scars from interactions with killer whale predators were ∼8× more common than scars from fishery interactions (3.9 vs. 0.5%), suggesting that predation is a much bigger threat to Pacific white-sided dolphins in the study area than anthropogenic impacts, or that dolphins are much less likely to survive a fishery interaction than a predation attempt. To advance our knowledge on poorly studied species, multiple lines of evidence will be needed.
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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 | 0.001 |
| 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".