Passive acoustic monitoring of Southern Resident Killer Whales: Occurrence, residency and movement data in support of conservation
Bibliographic record
Abstract
Passive acoustic monitoring (PAM) of cetaceans offers valuable spatio-temporal information on presence and residency time to support population management and recovery strategies. Acoustic detections can be used to evaluate diel and seasonal occurrence patterns when visual detection opportunities are limited (e.g., night or winter), and acoustic encounter duration can inform habitat use patterns (e.g., travelling/shorter duration vs foraging/longer duration). Additionally, detection data from strategically placed recorders may also be used in the development of predictive movement models. Here we use PAM data to evaluate critical habitat use by the Endangered Southern Resident Killer Whale (SRKW) population in Juan de Fuca Strait and the Salish Sea, near the southwest coast of Vancouver Island, British Columbia. Continuous recordings were collected at three locations from May to October, 2018–2020, and used to assess presence and acoustic encounter duration. As this area experiences significant commercial vessel traffic and recreational vessel use, mitigation of risk from acoustic disturbance is a high priority for recovery. These PAM detection and encounter duration data were used to provide advice for management actions in support of SRKW recovery in Canadian Pacific waters.
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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 | 0.001 | 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) | 0.001 | 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".