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Record W2189844683 · doi:10.3955/046.089.0207

Changes in the Occurrence and Behavior of Mammal-Eating Killer Whales in Southern British Columbia and Washington State, 1987–2010

2015· article· en· W2189844683 on OpenAlexaboutno aff
Juliana Houghton, Robin W. Baird, Candice K. Emmons, M. Bradley Hanson

Bibliographic record

VenueNorthwest Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsWhalePredationMarine mammalAbundance (ecology)MammalPopulationEcologyGeographyBiologyPredatorDemography

Abstract

fetched live from OpenAlex

The primary prey species of mammal-eating killer whales in the Salish Sea, the inland waters of southern British Columbia and Washington state, have experienced dramatic increases in population abundances in the last 25 years. It is possible that changes in prey abundance over time have resulted in changes in predator spatial use, occurrence and group size. Focused studies of mammal-eating killer whale behavior in the area were undertaken from 1987–1993, and an extensive record of sightings with confirmed identifications was available from 2004–2010. Changes in occurrence across years, months, and subareas of the Salish Sea were examined as well as changes in group size and in the identity of specific matrilines using the area. Occurrence of mammal-eating whales increased significantly from 2004–2010 with different seasonal peaks compared to 1987–1993. Different matrilines occurred in different seasons, time periods, and subareas. Group size was larger in 2004–2010 than in 1987–1993. The whales may be increasing use of the area due to increasing prey abundance or an overall increase in the whale population size. Changes in seasonal patterns of occurrence and the increase in group size between the two periods could be due to increased prey diversity.

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)0.000
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 score0.545
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.025
GPT teacher head0.241
Teacher spread0.217 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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