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Record W2098101522 · doi:10.1242/jeb.100347

Behavioral and metabolic contributions to thermoregulation in freely swimming leatherback turtles at high latitudes

2014· article· en· W2098101522 on OpenAlexaffabout
James Casey, Michael C. James, Amanda Southwood Williard

Bibliographic record

VenueJournal of Experimental Biology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersShandong Academy of Sciences
KeywordsForagingDiel vertical migrationThermoregulationLatitudePredationBiologyIngestionEcologyRange (aeronautics)EquatorEnvironmental scienceOceanographyFisheryGeographyGeology

Abstract

fetched live from OpenAlex

Leatherback turtles in the Northwest Atlantic Ocean have a broad geographic range that extends from nesting beaches near the equator to seasonal foraging grounds as far north as Canada. The ability of leatherbacks to maintain core body temperature (Tb) higher than that of the surrounding water is thought to be a key element of their biology that permits them to exploit productive waters at high latitudes. We provide the first recordings of Tb from freely swimming leatherbacks at a northern foraging ground, and use these data to assess the importance of behavioral adjustments and metabolic sources of heat for maintenance of the thermal gradient (Tg). The mean Tb for individual leatherbacks ranged from 25.4 ± 1.7 to 27.3 ± 0.3 °C, and Tg ranged from 10.7 ± 2.4 to 12.1 ± 1.7 °C. Variation in mean Tb was best explained by the amount of time that turtles spent in the relatively warm surface waters. A diel trend in Tb was apparent, with daytime cooling suggestive of prey ingestion and night-time warming attributable to endogenous heat production. We estimate that metabolic rates necessary to support the observed Tg are ~3 times higher than resting metabolic rate, and that specific dynamic action is an important source of heat for foraging leatherbacks.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.283

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 studies0.0000.000
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 head0.013
GPT teacher head0.290
Teacher spread0.276 · 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

Citations41
Published2014
Admission routes2
Has abstractyes

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