EFFECT OF THE SOUND GENERATED BY AN ACOUSTIC HARASSMENT DEVICE ON THE RELATIVE ABUNDANCE AND DISTRIBUTION OF HARBOR PORPOISES (<i>PHOCOENA PHOCOENA</i>) IN RETREAT PASSAGE, BRITISH COLUMBIA
Bibliographic record
Abstract
Abstract We describe an experiment conducted to assess the impact of the sound generated by an acoustic harassment device (AHD) on the relative abundance and distribution of harbor porpoises (Phocoena phocoena) in Retreat Passage, British Columbia. During control periods when the AHD was inactive, the mean number of porpoises observed in the study area was 0.39 for broad area scans conducted with the naked eye and 0.48 for narrow sector scans conducted with binoculars. Abundance declined precipitously when the AHD was activated, to 0.007 porpoises per broad area scan and 0.018 per narrow sector scan. The mean number of porpoise resightings while tracking their movements also declined from 12.2 to 13.6 per sighting during control periods to 1.1–1.9 per sighting when the AHD was activated, which suggested that the few porpoises that ventured into the study area spent less time within it when the AHD was activated. The effect of the AHD diminished with distance. No porpoises were observed within 200 m of the AHD when it was activated. The number of sightings and resightings observed when it was activated was less than 0.2% of the number expected had there been no AHD effect at a range of 200–399 m, 1.4% the number expected at a range of 400–599 m, varied between 2.5% and 3.3% of the number expected at a range of 600–2,499 m, and was 8.1% the number expected at a range of 2,500–3,500 m, which suggested that the impact of the AHD extended beyond our maximum sighting range of 3.5 km.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".