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Record W2019410641 · doi:10.1578/am.36.1.2010.67

Sequential Habitat Use by Two Resident Killer Whale (<I>Orcinus orca</I>) Clans in Resurrection Bay, Alaska, as Determined by Remote Acoustic Monitoring

2010· article· en· W2019410641 on OpenAlexaff
Harald Yurk, Olga A. Filatova, Craig O. Matkin, Lance Barrett‐Lennard, Michael Brittain

Bibliographic record

VenueAquatic Mammals · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBayWhaleClanFisheryHabitatGeographyOceanographyBiologyEcologyArchaeologyGeologySociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.017
GPT teacher head0.262
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2010
Admission routes1
Has abstractyes

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