NORTHERN RESIDENT KILLER WHALE RESPONSES TO VESSELS VARIED WITH NUMBER OF BOATS
Bibliographic record
Abstract
Vessel traffic has been implicated as a potential contributing factor to the at-risk status of two killer whale populations in western Canada and the US. Whalewatching guidelines can help mitigate this potential threat, especially when these are developed using experimental impact assessments that allow animal response to inform vessel management. Two published experimental studies on one of these populations documented stereotyped avoidance responses. Opportunistic observations in these studies in the mid-1990s suggested an inflection point in avoidance behavior when approximately 3 boats approached whales to within 1000m. Our experiment was designed to test whether whales responded differently to approach by few (1-3) versus many (>3) vessels. Data were collected in summer 2004, in Johnstone Strait, British Columbia (BC), Canada, using a theodolite to track positions of boats and individually identifiable whales. Experimental trials included 20-minute “no boat ” and 20-minute “boat ” phases (with local whalewatching vessels volunteering to act as experimental treatments), during which data were collected continuously on the focal whale. Responses of the 16 adult male killer whales tracked differed significantly between treatment levels (Wilcoxon’s test P=0.0148). Swimming path
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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".