Effect of interactive threats on foraging effort by endangered killer whales (<i>Orcinus orca</i>)
Bibliographic record
Abstract
Killer whales face many anthropogenic threats including vessel traffic, noise, and reduced prey availability. Here, we use high-resolution suction-cup Dtags to study the behavior of endangered Southern Resident killer whales that rely on biosonar to hunt salmon, and investigate how proximate vessels affect foraging behavior. From tag data, we identified subsurface behavior, including foraging and prey capture events. We then tested several vessel and associated sound, demographic, and environmental variables on behavioral state occurrence, time spent within each state, and foraging effort involving prey capture. Whales made fewer prey capture dives and spent less time in these dives when vessels had an average distance <400 yard (366 m). Lower prey abundance and higher vessel speed reduced prey capture probability, documenting the interplay between these effects. Finally, whales dove to depth more slowly while increasing dive duration to capture prey in the presence of vessel-emitted sonar, but descended more quickly with higher noise levels and closer vessels. Current efforts investigating foraging behavior and noise exposure over the diel cycle aim to better quantify foraging rates and activity budgets in this endangered population. These findings advance awareness of vessels and noise consequences on killer whales to inform conservation and management actions.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".