Acoustic risk factors and passive acoustic monitoring of endangered southern resident killer whales (<i>Orcinus orca</i>)
Bibliographic record
Abstract
Southern resident killer whales (SRKWs) occur along the coastal and inland waters of the northeast Pacific Ocean. They are currently listed as endangered in both the U.S. and Canada. Risk factors that are potentially affecting population recovery include prey availability and quality and acoustic disturbance. This is because SRKWs specialize on Chinook salmon, of which many stocks are depleted, and there is a well developed whale-watching industry that focuses on viewing SRKWs in their core summer habitat. This review paper will highlight acoustic research conducted to address risk factors, recovery goals, and other conservation considerations of this endangered population. These include investigations on acoustic and behavioral responses to anthropogenic sounds. A previous study demonstrated amplitude compensation as vessel noise increased and an ongoing study is investigating potential acoustic effects on behavior, including foraging, using suction cup attached digital acoustic recorders (DTAGs). This paper will also discuss passive acoustic monitoring efforts involving coastal ship surveys and acoustic recorder deployments which aim to better characterize critical habitat, particularly in areas and during seasons when SRKW occurrence is less well defined. All of these investigations are designed to provide critical data necessary to address and refine recovery goals and management actions for SRKWs.
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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 | 0.000 | 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".