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Record W2108222946 · doi:10.1139/z05-007

Factors influencing the diving behaviour of fish-eating killer whales: sex differences and diel and interannual variation in diving rates

2005· article· en· W2108222946 on OpenAlexvenueaboutno aff
Robin W. Baird, M. Bradley Hanson, Lawrence M. Dill

Bibliographic record

VenueCanadian Journal of Zoology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersNational Marine Fisheries Service
KeywordsBiologyDiel vertical migrationPredationSexual dimorphismPopulationAbundance (ecology)PredatorWhaleEcologyZoologyDemography

Abstract

fetched live from OpenAlex

Diving behaviour of air-breathing vertebrates may be influenced by a variety of factors including age, body size, and changes in prey behaviour and (or) abundance over both short and long timescales. We studied the diving behaviour of a highly sexually dimorphic odontocete cetacean, the killer whale, Orcinus orca (L., 1758), using suction-cup-attached time-depth recorders (TDRs). We tested the hypotheses that dive rates (no. of dives/h greater than or equal to specific depths) of fish-eating killer whales varied between males and females, with age, between day and night, and among pods and years. Data were used from 34 TDR deployments between 1993 and 2002 in the inshore waters of southern British Columbia, Canada, and Washington, USA. Dive rates did not change with age or differ among pods or between males and females, although analyses restricted to adults showed that adult males dove deep significantly more frequently than adult females during the day. For all whales, dive rates and swim speeds were greater during the day than at night, suggesting decreased activity levels at night. Dive rates to deeper depths during the day decreased over the study, suggesting a long-term change in prey behaviour or abundance, though uncertainty regarding the diet of this population precludes determination of the cause of such changes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.018
GPT teacher head0.227
Teacher spread0.208 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations119
Published2005
Admission routes2
Has abstractyes

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