Passive acoustic detection and localization of whales: Effects of shipping noise in Saguenay–St. Lawrence Marine Park
Bibliographic record
Abstract
The performance of large-aperture hydrophone arrays to detect and localize blue and fin whales' 15-85 Hz signature vocalizations under ocean noise conditions was assessed through simulations from a normal mode propagation model combined to noise statistics from 15 960 h of recordings in Saguenay-St. Lawrence Marine Park. The probability density functions of 2482 summer noise level estimates in the call bands were used to attach a probability of detection/masking to the simulated call levels as a function of whale depth and range for typical environmental conditions. Results indicate that call detection was modulated by the calling depth relative to the sound channel axis and by modal constructive and destructive interferences with range. Masking of loud infrasounds could reach 40% at 30 km for a receiver at the optimal depth. The 30 dB weaker blue whale D-call were subject to severe masking. Mapping the percentages of detection and localization allowed assessing the performance of a six-hydrophone array under mean- and low-noise conditions. This approach is helpful for optimizing hydrophone configuration in implementing passive acoustic monitoring arrays and building their detection function for whale density assessment, as an alternative to or in combination with the traditional undersampling visual methods.
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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 | 0.000 | 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) | 0.000 | 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".