Acoustic recording systems for baleen whales and killer whales on the west coast of Canada
Bibliographic record
Abstract
The threat to the survival of several whale species and the introduction of the Species at Risk Act (SARA) has highlighted the need for better knowledge about the biology and ecology of marine mammals in Canadian waters. The North Pacific right whale (Eubalaena japonica), once plentiful across much of the North Pacific Ocean, is now rarely seen in coastal British Columbian waters, and the number of killer whales (Orcinus orca) in southern British Columbia has been steadily decreasing in recent years. The recovery plan for these species is based on the gathering of baseline data on occurrence, distribution, abundance and habitat, and one significant component of this data collection is based on the deployment of multiple passive acoustical recording systems off the coast of British Columbia. In addition to the development and use of a simple but effective two-hydrophone array, two different autonomous passive acoustical instruments have been developed, one deployable at shore sites and the other for offshore locations. To limit data storage and power requirements, both of these systems have been equipped with killer whale recognition hardware to record only when the probability of killer whales in the area is relatively high. In addition the offshore units have been designed as hybrid recorders, sampling at 1000 Hz for the larger baleen whales and 20 kHz when killer whales are present. Both of these instruments have been designed for deployment periods as long as 12 months and are presently deployed in locations along the BC coast. The analysis of the large data sets from these instruments is a challenge and we are currently investigating the use of neural network algorithms to perform not only species recognition but also, with regards to the killer whale population, clan or group identification. The goal is to adapt these algorithms directly into the self-contained instruments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".