A sound approach to killer whale conservation: understanding and protecting the ocean’s top predator
Bibliographic record
Abstract
Professor of Wildlife Conservation at the University of Cumbria, Volker Deecke will present the results of research using innovative digital recording tags to understand the foraging behaviour of mammal-hunting and fish-eating killer whales in the Northeast Pacific. Killer whales are the top predators in marine ecosystems and are found in all the world’s oceans, yet we know surprisingly little about how these animals communicate and find their prey. Volker’s research shows how new technology can help us understand how these animals communicate and find and catch their prey, and how underwater noise may be interfering with these essential life processes. Born in Germany and raised in Austria, Volker Deecke received his BSc and Masters from the University of British Columbia and a Doctorate from the University of St. Andrews. He has studied killer whales and other marine mammals in Canada, Alaska, Iceland and Shetland. He is interested in the role of behavioural research in wildlife conservation, specifically understanding underwater communication and the effect of underwater noise.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".