Integrating visual and acoustic observations to build an “intelligent” killer whale movement forecast system
Bibliographic record
Abstract
Shipping traffic continues to grow in the Salish Sea with considerable marine industrial developments planned for the region. With this comes an increase in the risk of cetaceans being disturbed, harassed, and potentially colliding with commercially operating vessels. One key conservation concern is these waters coincide with designated Critical Habitat for the endangered population of Southern Resident killer whales (SRKW). Monitoring technologies such as hydrophones provide the promise of real-time animal detection and localisation. To use these continuous streams of real-time whale location data, we are developing algorithms that automate acoustic detection and classification of SRKW calls using deep learning models trained on extensive new and updated annotated datasets from the region. We are developing open-source models for pod-level classification, while also differentiating SRKW from other ecotypes and species. We are developing a forecasting system to merge these whale detections with opportunistic visual observations. This is based on sequential data assimilation methods using advanced animal movement models to estimate SRKW pod locations with probabilistic predictions of whale directional movement. These methods will be transformative for shipping by providing forecasting with a lead time of a few hours allowing vessels to adjust their speed or pathway to minimize whale-vessel interactions.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".