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Foraging behavior of adult female Steller sea lions during the breeding season in Southeast Alaska

2009· article· en· W1997093746 on OpenAlexfundno aff
M. J. Rehberg, Russel D. Andrews, Una G Swain, D. G. Calkins

Bibliographic record

VenueMarine Mammal Science · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersNational Marine Fisheries ServiceUniversity of British Columbia
KeywordsForagingRookeryBiologyPredationPopulationEcologyNesting seasonSeasonal breederStock (firearms)HabitatFisheryGeographyDemography

Abstract

fetched live from OpenAlex

Abstract During the 1990s, the Steller sea lion ( Eumetopias jubatus Schreber) Western Alaska stock (WS) suffered steep population decline while the Eastern Alaska stock (ES) steadily increased. One bottom‐up forcing hypothesis explaining this decline predicted lactating adult female foraging behavior would be different between stocks. To investigate this effect, we monitored 11 ES females at two breeding rookeries using satellite dive recorders (SDR) during the early breeding seasons of 1992–1993, examined their behavior with respect to prey, physiological limitations, and habitat, and made limited comparisons to observations of WS female behavior reported in the literature. ES females were not operating at the extremes of ability, with most diving within the limits of aerobic metabolism, less than one‐quarter of possible foraging time during trips spent submerged and most foraging trips requiring less than one‐half the lipid store fasting ability of dependent pups. Thus, females may have some capacity to alter behavior to accommodate future changes in foraging conditions, but the extent of this plasticity is unknown. Because recent work suggests WS recovery is impeded by low natality, future studies should test differences between reproductive and non‐reproductive mature females in order to properly assess the contribution of foraging ecology to SSL population dynamics.

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 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 score0.063
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Citations32
Published2009
Admission routes1
Has abstractyes

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