Vocalisation rates of the North Atlantic right whale (Eubalaena glacialis)
Bibliographic record
Abstract
Vocalisation rates were measured from North Atlantic right whales (Eubalaena glacialis) in spring 1999-2000 in the Great South Channel and off Cape Cod, USA, and in summer 1999-2000 in the Bay of Fundy, Canada. Vocalisations were classed as either ‘moans’, ‘low-frequency (LF) calls’ or ‘gunshots’. Towed hydrophone recordings (36.1 hours) were made in 21 encounters where loose aggregations of right whales were within about 1,000m. Recordings were also made using acoustic tags attached by suction cups to ten different whales (29.5 hours). Tags also recorded depth data. Moan rates (sounds per aggregation per hour) were correlated with size of whale aggregation. Individual whales produced moans at ~ 0-10 per hour (recorded from tags and the towed hydrophone). Small aggregations (2-10) gave higher moan rates (usually < ~ 60 per hr) and larger aggregations ( > 10) higher still ( ~ 70-700 per hr) (recorded from towed hydrophone). Results from the Bay of Fundy indicate high moan rates at night. Moans were usually produced in clusters. Tag data showed that moans were usually produced when whales were within about 10m of the surface. A passive acoustic system could potentially provide supplementary information on the distribution of aggregations of right whales. This could be useful for management (1) in the long term, by aiding the prediction of right whale distribution, or (2) as a real-time tool for helping to route shipping away from concentrations of right whales. The empirical evidence presented here on vocalisation rates will assist in assessing feasibility. The clustering of moans and the tendency to produce them near the surface could hamper detection and localisation efforts. Further research is underway to investigate other important practical issues such as detectability and source levels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".