Automated localization of whales in coastal fjords
Bibliographic record
Abstract
Localization and tracking of vocalizing marine mammals are powerful tools for understanding and mitigating the impacts of anthropogenic stressors such as vessel noise on habitat use of cetaceans. A large-aperture hydrophone network has been installed in the Kitimat Fjord System, an ecologically, culturally, and economically valued marine environment in northern British Columbia, Canada. This network consists of four synchronized bottom-mounted hydrophones that permanently record and radio-transmit data to a land-based laboratory. An automated system has been developed which includes routines to localize transient bio-acoustic signals from three or more streaming hydrophones in near real-time. These routines comprise the correlation of hydrophone signals, the construction of a time lag model, and signal localization and error estimation from a spatial likelihood surface. The localization method was tested experimentally and subsequently applied to vocalizations from humpback whales, fin whales, and killer whales. Refractive and reflective sound propagation effects in the confined fjords are assessed using ray tracing propagation models. Automated localization results are compared to ground-truth data and shown to provide good accuracy.
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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 | 0.001 | 0.001 |
| 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".