Directional detection of simulated whale calls and ambient noise in a busy harbour
Bibliographic record
Abstract
Timely information on marine mammal presence in, or near, sonar training areas can be used to minimize the risk of harming these animals. Defence Research and Development Canada (DRDC) has recently been investigating a variety of acoustic technologies to facilitate real-time marine mammal monitoring in high noise (including ship noise and sonar signals) environments. DRDC assessed the directional detection and localization capability of a network of sub-surface volumetric and vertical line arrays against the noisy backdrop of the busy harbour during an experiment conducted in Bedford Basin, NS. Playbacks of Southern Resident killer whale calls and North Atlantic right whale upcalls, as well as simulated calls, were transmitted. To assess variability in bearing estimates for whale calls in different frequency bands, an acoustic source was fixed at a single location for half of the experiment and dipped over the side of a boat at seven locations around the recorders during the other half. During this talk, ambient noise and environmental measurements will be presented along with acoustic localization results.
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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 | 0.001 | 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) | 0.001 | 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".