Sequential Habitat Use by Two Resident Killer Whale (<I>Orcinus orca</I>) Clans in Resurrection Bay, Alaska, as Determined by Remote Acoustic Monitoring
Bibliographic record
Abstract
Killer whales (Orcinus orca) are sighted regularly in coastal Alaska during the summer, but little is known about their movements through the area during the winter when weather and light limit the use of boat-based surveys. Acoustic monitoring provides a practical alternative because each extended resident killer whale family group or pod has a unique dialect that can be discerned by differences in their repertoires of stereotyped calls. The repertoires of resident killer whale pods in the northern Gulf of Alaska were updated from earlier studies, and the results used to determine the identity of pods that were recorded on remote hydrophones in Resurrection Bay, Alaska, in the fall, winter, and spring of 1999 to 2004. In total, seven pods of resident killer whales were identified acoustically, comprising four related pods from AB clan and three from AD clan. The frequencies of occurrence of the clans differed between the November to March recording period when AB clan occupied the area, and the April-May period when AD clan was predominant. The sequential use of this habitat during periods of relative prey scarcity has the effect of limiting intergroup resource competition and is consistent with earlier findings that demonstrated divergent resource specialization by sympatric killer whale populations.
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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".