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Record W2135068634 · doi:10.3354/meps08447

Changes in the foraging dive behaviour and energetics of king penguins through summer and autumn: a month by month analysis

2009· article· en· W2135068634 on OpenAlexaff
Lewis G. Halsey, P. J. Butler, Andreas Fahlman, CA Bost, Yves Handrich

Bibliographic record

VenueMarine Ecology Progress Series · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsNorth Pacific Marine Science Organization
Fundersnot available
KeywordsEnergeticsForagingSeasonal breederGeographyEcologyMammalBiology

Abstract

fetched live from OpenAlex

King penguins Aptenodytes patagonicus are known to change their diving behaviour in response to changes in both prey location and their breeding status through the early stages of the breeding cycle (austral summer and autumn). However, little information exists on whether and how these changes affect the energy expenditure of such behaviour. By deploying heart rate and hydrostatic pressure data loggers, we investigated detailed changes in the dive time budgeting of king penguins during foraging dives across the breeding season, in the same individuals, and the associated changes in estimated oxygen consumption during those dives. Maximum dive depth, duration, bottom duration, feeding events (indicated by wiggles) per dive and post-dive duration increased through the study period. While a foraging dive later in the breeding season was energetically more costly than a dive earlier in the season, the overall rate of energy expenditure did not change, nor did energy cost per unit prey capture. These findings indicate an ability of king penguins to adjust their foraging dive behaviours through the summer and autumn without affecting the energetic costs of diving to capture prey. Such plasticity may be necessary to compensate for changes both in prey location and abundance, and in the energy requirements of the chick over time.

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 score0.071
Threshold uncertainty score0.946

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 science0.0000.000
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 head0.009
GPT teacher head0.234
Teacher spread0.226 · 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
Published2009
Admission routes1
Has abstractyes

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