Temporal patterns in Pacific white-sided dolphin pulsed calls at Barkley Canyon, with implications for multiple populations
Bibliographic record
Abstract
Evaluation of diel and seasonal patterns in offshore marine mammal activity through visual data collection can be impaired by poor weather and light limitations and by the requirement for costly ship time. As a result, relatively little is known about the diel patterns of wild dolphins. Pacific white-sided dolphins north of Southern California are particularly under-researched. Collecting acoustic data can be a cost-effective approach to evaluating activity patterns in offshore marine mammals. However, manual analysis of acoustic data is time-consuming, and impractical for large data sets. This study evaluates diel and seasonal patterns in Pacific white-sided dolphin communication through automated analysis of one year of continuous acoustic data collected from the Barkley Canyon node of Ocean Networks Canada’s NEPTUNE observatory, offshore Vancouver Island, British Columbia, Canada. In this study, marine mammal acoustic signals are manually annotated in a sub-set of the data, and used to train a random forest classifier targeting Pacific white-sided dolphin pulsed calls. Marine mammal vocalizations are classified using the resultant classifier, manually verified, and examined for seasonal and diel patterns. Pacific white-sided dolphins are shown to be vocally active during all diel periods in the spring and summer, but primarily at dusk and night in the fall and winter. Additionally, the percentage of time they are detected drops significantly in the fall and remains low during the winter. This pattern suggests that a group of day-active dolphins, possibly a unique population, leaves Barkley Canyon in the fall and returns in the spring. It is hypothesized that this group may be following the Pacific herring, which are present at the surface during the day at Barkley Canyon in the spring and summer, and migrate inshore for the fall and winter.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".