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Record W3085908400 · doi:10.1111/mms.12743

Resting and swimming metabolic rates in juvenile walruses (<scp><i>Odobenus rosmarus</i></scp>)

2020· article· en· W3085908400 on OpenAlexafffund
David A. S. Rosen

Bibliographic record

VenueMarine Mammal Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsVancouver AquariumUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsJuvenileForagingPredationBiologyBioenergeticsBasal metabolic rateMarine mammalEcologyEnergeticsEnergy expenditureArcticZoology

Abstract

fetched live from OpenAlex

Abstract Changes in Arctic ice conditions have raised concerns regarding potential impacts on energy expenditure and food requirements of walruses. Modeling the repercussions of environmental changes requires accurate species‐specific measures of bioenergetic expenditures. This is particularly true for walruses, who have a unique anatomy and foraging ecology from other pinnipeds. This study measured resting metabolic rate (RMR) and subsurface swimming metabolism in two juvenile walruses over a 13‐month period. The walruses had relatively low RMR compared to studies of other young pinnipeds. RMR was greater for the male than the female, as expected given its larger size; the reverse was true on a mass‐specific basis. There was also considerable variability in RMR for each walrus during the year that could not be accounted for by changes in body mass. Metabolism while swimming was about twice RMR, and locomotor costs were higher than generally predicted for other marine mammals. The lower calculated swimming efficiency may reflect the fact that walruses are not “high velocity” pursuit predators. The estimates of metabolic expenditure obtained in this study for young walruses are invaluable for quantifying the energetic consequences of behavioral changes induced by environmental shifts in the wild.

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)0.002
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 score0.181
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.005
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 head0.023
GPT teacher head0.242
Teacher spread0.219 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

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