Photo-identification catalogue and status of the coastal subset of the West Coast Transient population of Bigg’s killer whale in British Columbia, Canada
Bibliographic record
Abstract
Bigg’s killer whales have been photo-identified in British Columbia for decades. This report uses a 67-year archive of photo-identification data from 1958-2024 to first examine trends in data collection, occurrence and discovery of unique Bigg’s killer whales within the province. In total, 945 individuals known from BC, 571 of which were alive in 2024, were photo-identified during 10,390 encounters between California and Alaska. The overall number of encounters and individuals identified each year increased steadily during the study period. Secondly, three criteria including number of years since last documented, total number of encounters and years documented were used to define a subset of individuals in the threatened West Coast Transient population, which due to their preferences for coastal waters is most likely to be impacted by human activities. This coastal subset (CS) included 385 individuals of which 243 were sexually mature in 2024. Third, by including known and assumed deceased kin, growth for the CS was retrospectively calculated at an average annual rate of 1.9% between 2019 and 2024 and 2.9% between 1994 and 2024. Finally, identification images of the left and right sides of dorsal fins, saddle patches and eyepatches for each CS individual alive to date in 2025 are provided along with information on birth years, sex, maternal ancestry and social cohesion.
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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 | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".