Echolocation signals of free-ranging killer whales (<i>Orcinus orca</i>) and modeling of foraging for chinook salmon (<i>Oncorhynchus tshawytscha</i>)
Bibliographic record
Abstract
Fish-eating "resident"-type killer whales (Orcinus orca) that frequent the coastal waters off northeastern Vancouver Island, Canada have a strong preference for chinook salmon (Oncorhynchus tshawytscha). The whales in this region often forage along steep cliffs that extend into the water, echolocating their prey. Echolocation signals of resident killer whales were measured with a four-hydrophone symmetrical star array and the signals were simultaneously digitized at a sample rate of 500 kHz using a lunch-box PC. A portable VCR recorded the images from an underwater camera located adjacent to the array center. Only signals emanating from close to the beam axis (1185 total) were chosen for a detailed analysis. Killer whales project very broadband echolocation signals (Q equal 0.9 to 1.4) that tend to have bimodal frequency structure. Ninety-seven percent of the signals had center frequencies between 45 and 80 kHz with bandwidths between 35 and 50 kHz. The peak-to-peak source level of the echolocation signals decreased as a function of the one-way transmission loss to the array. Source levels varied between 195 and 224 dB re: 1 microPa. Using a model of target strength for chinook salmon, the echo levels from the echolocation signals are estimated for different horizontal ranges between a whale and a salmon. At a horizontal range of 100 m, the echo level should exceed an Orcinus hearing threshold at 50 kHz by over 29 dB and should be greater than sea state 4 noise by at least 9 dB. In moderately heavy rain conditions, the detection range will be reduced substantially and the echo level at a horizontal range of 40 m would be close to the level of the rain noise.
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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 | 0.000 | 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".