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Record W2029432264 · doi:10.1111/mms.12049

Life history and population dynamics of southern Alaska resident killer whales (<i>Orcinus orca</i>)

2013· article· en· W2029432264 on OpenAlexaff
Craig O. Matkin, J. Ward Testa, Graeme M. Ellis, Eva Saulitis

Bibliographic record

VenueMarine Mammal Science · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans Canada
FundersAlaska Sea Grant, University of Alaska FairbanksExxon Valdez Oil Spill Trustee Council
KeywordsPopulationArchipelagoGeographyPopulation growthPopulation declineFisheryEcologyBiologyDemography

Abstract

fetched live from OpenAlex

Abstract Resident (fish eating) killer whales (Orcinus orca ) in the North Pacific have been the subject of long‐term studies in several geographical regions. The current study examines population parameters in the southern Alaska resident population from 1984 to 2010 and develops a population model. The southern Alaska resident population ranges from southeastern Alaska through the Kodiak archipelago and contains over 700 individuals. We follow the life histories of 343 identifiable whales in 10 pods from two clans born before and during the study. Population parameters were comparable to those of the British Columbia northern resident population during the 1970s and 1980s, except that age of maturity was approximately one year earlier. The average annual rate of increase was slightly higher in Alaska (3.5%) than for the British Columbia northern residents (2.9%) and probably represents a population at r‐max (maximum rate of growth). Reasons for the high growth rate in Alaska could be a recovery following past anthropogenic mortalities, or more likely, a response to increasing salmon returns in recent decades, resulting in an increase in carrying capacity. The slow maturation and low rate of reproductive response makes these whales slow to recover from natural or anthropogenic catastrophes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.993

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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.

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

Citations52
Published2013
Admission routes1
Has abstractyes

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