Capturing and Handling of White Whales (<i>Delphinapterus leucas</i>) in the Canadian Arctic for Instrumentation and Release
Bibliographic record
Abstract
For many decades, humans have captured white whales (Delphinapterus leucas) for food, research, and public display, using a variety of techniques. The recent use of satellite-linked telemetry and pectoral flipper band tags to determine the movements and diving behaviour of these animals has required the live capture of a considerable number of belugas. Three principal techniques have been developed; their use depends on the clarity and depth of the water, tidal action, and bottom topography in the capture area. When the water is clear enough so that the whales can be seen swimming under the water and herded into shallow sandy areas, a hoop net is placed over the whale's head from an inflatable boat. When the water is murky and the belugas cannot easily be seen under the water, but can be herded into relatively shallow sandy areas, a seine net is deployed from a fast-moving boat to encircle them. If the whales are in deep water and cannot be herded into shallow water, a stationary net is set from shore to entangle them. Once captured, the whales have to be restrained in a way that allows them to breathe easily, have the tags attached, and be released as quickly as possible. The methods have proved to be safe, judging from the whales' rapid return to apparently normal behavioural patterns.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".