Killer whales: the natural history and genealogy of Orinus orca in British Columbia and Washington
Bibliographic record
Abstract
Watching killer whales in the wild in British Columbia and Washington state has become a popular recreational activity in the last decade. Nothing quite matches the thrill of witnessing a pod of these immense creatures cutting through the waters of Johnstone Strait or listening to their strident underwater calls to each other in their own dialect. The new edition of this book presents updated results of over 25 years of killer whale research in British Columbia and Washington. Intended for both whale enthusiasts and researchers, it contains the latest information on what is known of killer whale natural history and presents a catalogue of close to 300 photographs of resident killer whales as well as a genealogical registry that enables readers to identify individual killer whales and their family groups. New in this edition is the latest information on the status and future prospects of west-coast killer whales - how they may be affected by declining salmon stocks, high levels of toxic chemicals in their tissues, and increasing vessel traffic and underwater noise. Whale watchers will particularly appreciate revised suggestions and guidelines on how to view whales in the wild without disturbing them. The authors are active researchers who are widely regarded as the world's foremost authorities on killer whales.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| 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".