Porpoises after dark: Seasonal and diel patterns in Pacific harbour porpoise (Phocoena phocoena vomerina) aggregations at one of North America’s fastest growing ports
Bibliographic record
Abstract
Pacific Harbour Porpoise ( Phocoena phocoena vomerina ) occupy a large range throughout coastal waters of British Columbia. Despite their wide distribution, they remain largely data-deficient regarding abundance and population trends, and as such are listed as Special Concern under the Species At Risk Act. Harbour porpoises are also particularly sensitive to disturbance, especially vessel-related acoustic disturbance. Large aggregations of harbour porpoise have been documented in waters around the entrance to the Port of Prince Rupert during the winter months, however little is known about the annual fine-scale activity of this species in this highly trafficked area. In this multi-year study, we used a combination of land-based visual surveys and passive acoustic monitoring (PAM) devices (C-PODs and F-PODs) to address data gaps regarding density, diel patterns, and seasonality of harbour porpoise around Prince Rupert. Echolocation activity was detected during 96% of the 1086 C-POD deployment days and 100% of the 727 F-POD deployment days, with 86% of visual surveys recording harbour porpoise presence. We detected strong seasonal and diel trends in activity, with echolocation peaks between April and June and during the hours of darkness throughout the year. There was a notable increase in daytime activity of harbour porpoise between January and March, which coincides with the months of large aggregation observations. This study indicates that despite the constant presence of large vessels, harbour porpoise continue to persist within waters surrounding Prince Rupert. This suggests the area is an important habitat for this species and also may indicate some extent of acclimatization to localized disturbance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".