Acoustic monitoring of resident, offshore, and transient killer whales off the Washington coast
Bibliographic record
Abstract
Three acoustically distinct populations of killer whales representing each of the known ecotypes (resident, offshore, transient) were recorded in the Summer–Fall of 2004 off the southern Olympic Coast of Washington. Two high-frequency acoustic recording packages (HARPs) continuously recording at 80-kHz sample rate were deployed to assess the seasonal occurrence of vocal odontocetes in this region. From mid-July to early-October the population-specific discrete calls of killer whales were heard on 8 days and were classified to population by Volker Deecke (UBC) and John Ford (DFO-Canada) using an acoustic ID catalogue. West Coast Transient killer whales producing calls of the California dialect were heard on three occasions from August through October. Offshore killer whales were heard twice in August–September, and Northern Resident killer whales were heard once in August. Although Northern Resident killer whales have been extensively studied within Puget Sound and coastal British Columbia, they have been visually sighted only once off the northern Olympic Peninsula, making their detection at this offshore southerly location unique. Endangered Southern Resident killer whales were not heard at this site from July–October. Analysis of year-round data from a site further offshore is underway. [Funded by Chief of Naval Operations- N45.]
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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 | 0.000 |
| 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".