Morphometrics of mammal‐eating killer whales from drone photogrammetry, with comparison to sympatric fish‐eating killer whales in the eastern North Pacific
Bibliographic record
Abstract
Abstract Aerial photogrammetry has provided increased power for monitoring the health of individuals in the endangered population of Southern Resident killer whales (SRKW, Orcinus orca ) in the eastern North Pacific. These data have shown evidence of nutritional stress, with individual growth and body condition correlating with the availability of their primary prey, Chinook salmon ( Oncorhynchus tshawytscha ). We used drones to derive similar but novel photogrammetry measurements from a sympatric population of mammal‐eating Bigg's killer whales (BKWs) that has been increasing in abundance in recent decades. From 2014 to 2019 we photographed 95 individual BKWs in Canadian waters off Vancouver Island and US waters in the Salish Sea; we estimated asymptotic lengths of 6.4 m for adult females and 7.3 m for adult males, both longer than corresponding length estimates for SRKWs. As a proxy for body condition, we measured head width at a standardized distance behind the blowhole, expressed as proportion of the length between the blowhole and dorsal fin, and estimated that on average, all age/sex classes of BKWs were more robust than corresponding classes of SRKWs. These differences likely reflect divergent adaptive selection in these prey‐specialist ecotypes, but may also partially indicate recent impacts of differential prey availability.
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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 | 0.001 | 0.001 |
| 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".