Localization of killer whale (Orcinus orca) vocalizations using a triangular hydrophone array in Johnstone Strait, British Columbia, Canada
Bibliographic record
Abstract
A triangular hydrophone array was bottom-mounted in Johnstone Strait, in summer 2006, to localize sounds emitted by individual or small groups of orcas. Spatial and temporal positioning of individual orcas was obtained from a cliff-site observation platform; a video camera recorded surface behaviors and associations while a theodolite was used to obtain vertical and azimuthal angles needed to calculate distances to the animals. The time of arrival differences of the orca sounds at each hydrophone was used to determine the angle of arrival of the sound. Sounds were then aligned in time and space with the orcas’ corresponding movement tracks. The ability to spatially and temporally localize the vocalizations and behaviors of an isolated orca provides insight into how an individual or group of individuals may function within a matrilineal unit or pod. The objective of this study is to determine if there are defined ‘‘roles’’ in the vocal behaviors of individuals (adult males, adult females, juveniles, calves) within small groups of orcas; specifically, individual differences in orca calling behavior that regulates group movement and changes in group behavior. Results from analysis of pod encounters will be discussed and correlation of vocalizations and specific individual behavior addressed.
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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 | 0.001 | 0.001 |
| 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".