Celebrating the Navy’s sea canaries: The lives and bioacoustic research accomplishments of white whale (<i>Delphinapterus leucas</i>) collaborators
Bibliographic record
Abstract
The Navy Marine Mammal Program has engaged many different species over the years to accomplish missions of finding and retrieving objects in the marine environment, and asset protection, but perhaps none has been so engaging as the white whale, or beluga (Delphinapterus leucas). At a time when the Cold War focus moved to the frigid waters of the Artic, the Navy needed echolocating, deep diving, cold-water experts. Enter, the belugas. Unique because of their all-white color, lack of a dorsal fin, and extensive repertoire of bird-like vocalizations (hence the moniker “sea canary”), the group with their naturally curious, gregarious demeanor and malleable melons, also quickly began to grace the pages of scientific journals and book chapters with their acoustic capabilities for a period of approximately 30 years. Most notably among them were a female named MUK (after the Inuit word muk tuk for whaleskin and blubber) and a male named NOC (after the tiny biting summer flies known as no-see-ums). Together, these two whales expanded our knowledge of marine mammal bioacoustics and physiology with their collaboration on projects related to detection, hearing, echolocation, nasal pressure, sound production, human-speech mimicry, net-aided foraging, diving physiology, stress hormones, reproduction, growth, and development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".