How acoustics informs understanding of foraging behavior and effects of vessels and noise on killer whales
Bibliographic record
Abstract
Foraging in toothed whales and dolphins is fundamentally tied to the use of sound. Resident-type killer whales (Orcinus orca) use echolocation to locate and capture fast-moving salmon and other fish prey. In addition to prey availability, disturbance from vessels and noise is a threat to the endangered Southern Resident killer whale population given considerable levels of commercial shipping, fishing, whale-watching and recreational vessel traffic in urban waterways that the whales use for feeding. In this study, we utilized suction cup-attached digital acoustic recording tags (DTAGs) to (1) describe whale acoustic and movement behavior during different phases of foraging that can be differentiated from other behaviors, (2) investigate vessel and noise effects on behavior and foraging outcomes in the endangered population, (3) compare foraging behavior between the endangered population that is struggling with population recovery and another population (Northern Resident killer whales) that is increasing in numbers, and (4) characterize diel patterns of foraging and other behaviors to describe their full activity budget and inform management of vessel traffic and noise during urban expansion along the Pacific Northwest coast of North America. This presentation will highlight results to date and implications for the conservation and management of marine protected species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 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".