Waveguide propagation allows range estimates for North Pacific right whales in the Bering Sea. Canadian Acoustics
Bibliographic record
Abstract
The shallow and uniform water depth of the eastern Bering Sea shelf results in an acoustic waveguide. Propagation within this waveguide produces waveform dispersion which is dependent upon range. We present a means for using dispersed waveforms to determine range to calling whales from a single autonomous acoustic recording instrument. The predominant North Pacific right whale (Eubalaena japonica) call is frequency upswept from about 90 Hz to around 160 Hz and lasts approximately 1 s. The regional bathymetry of the eastern Bering Sea middle shelf is relatively uniform and shallow ( ~ 70 meters deep). This geometry provides a plane-layered waveguide in which right whale upswept calls can be detected at ranges over 50 km and have multiple modal arrivals that become dispersed, displaying different propagation velocities for different frequencies. Dispersion characteristics of modal arrivals are dependent on the calling whale’s depth, the receiver’s depth, the water depth, the range from caller to receiver, and various environmental parameters including water and sediment density and sound velocity. A model of sound propagation for the eastern Bering Sea middle shelf is developed from right whale call dispersion recorded on sonobuoys and seafloor acoustic recording packages, using individual calls recorded at multiple instruments. After development of the model, waveform dispersion allows estimation of caller
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".