Spatial and temporal analysis of killer whale (<i>Orcinus orca</i>) strandings in the North Pacific Ocean and the benefits of a coordinated stranding response protocol
Bibliographic record
Abstract
Abstract Killer whales ( Orcinus orca ) are widely distributed throughout the world's oceans, yet little has been documented about their stranding patterns. Knowledge of stranding patterns improves our ability to examine and sample carcasses and provides a foundation for understanding killer whale natural history, diet, reproduction, anthropogenic stressors, emerging diseases, and patterns of unusual mortality. We compiled published and unpublished killer whale stranding data to describe stranding patterns in the North Pacific Ocean. Between 1925 and 2011, 371 stranded killer whales were reported in Japan (20.4%), Russia (3.5%), Alaska (32.0%), British Columbia (27.4%), Washington (4.0%), Oregon (2.7%), California (5.1%), Mexico (3.8%), and Hawaii (0.8%). Strandings occurred at all times of year, but regionally specific seasonal differences were observed. Mortality and annual census data from Northern and Southern Resident populations were extrapolated to estimate that across the North Pacific, an average of 48 killer whales die annually. However, over the last two decades, an average of only 10 killer whale carcasses were recovered annually in this ocean, making each event a rare opportunity for study. Publication of a standardized killer whale necropsy protocol and dedicated funding facilitated the number of complete postmortem necropsies performed on stranded killer whales from 1.6% to 32.2% annually.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".