Automated detection of Beluga vocalizations in Kugmallit Bay in 2019 to contextualize results of concurrent aerial surveys
Bibliographic record
Abstract
Beluga whales (Delphinapterus leucas) from the Eastern Beaufort Sea population migrate from overwintering locations in the Bering Sea to the Beaufort Sea in the spring and form large summering congregations in the Mackenzie Estuary. Beluga presence in the estuary typically follows ice breakup in late June, peaking in early to mid-July and tapering off in late-July and August. High-speed winds – more common in late-July and August – appear to cause belugas to leave the shallow areas which are most commonly used. This pattern of habitat use is well known by Inuvialuit who harvest whales in the area and has been observed during passive acoustic monitoring studies conducted in the region since 2011. In 2019, aerial surveys were conducted in the Canadian Beaufort Sea to assess beluga population numbers while two hydrophones were deployed in Kugmallit Bay (Mackenzie Estuary). The hydrophone data show that the inshore portion of the 2019 survey occurred after the peak timing of beluga estuarine use and amid high-speed wind events which likely caused belugas to leave the area. This may have led to an underestimation of the number of belugas that use inshore areas of the Mackenzie Estuary.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".