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Record W1975118928 · doi:10.1111/mms.12103

Broad thermal capacity facilitates the primarily pelagic existence of northern fur seals (<i>Callorhinus ursinus</i>)

2014· article· en· W1975118928 on OpenAlexaff
Alex Dalton, David A. S. Rosen, Andrew W. Trites

Bibliographic record

VenueMarine Mammal Science · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
FundersNational Oceanic and Atmospheric Administration
KeywordsFur sealPelagic zoneElephant sealEcologyEnvironmental scienceBiologyOceanographyGeology

Abstract

fetched live from OpenAlex

Abstract Thermoregulatory capacity may constrain the distribution of marine mammals despite having anatomical and physiological adaptations to compensate for the thermal challenges of an aquatic lifestyle. We tested whether subadult female northern fur seals ( Callorhinus ursinus ) experience increased thermoregulatory costs in water temperatures potentially encountered during their annual migration in the Bering Sea and North Pacific Ocean. Metabolic rates were measured seasonally in 6 captive female northern fur seals (2.75–3.5 yr old) in ambient air and controlled water temperatures of 2°C, 10°C, and 18°C. Rates of oxygen consumption in ambient air (1°C–18°C) were not related to environmental temperature except below 2.5°C (winter only). However, metabolism was significantly higher during the fall seasonal trials (September–October) compared to other times of year, perhaps due to the costs of molting. The fur seals appeared thermally neutral in all seasons for all water temperatures tested (2°C–18°C) except during the summer when metabolic rates were higher in the 2°C water. Comparing this broad thermal neutral zone to the average sea surface temperatures potentially encountered during annual migrations indicates wild fur seals can likely exploit a large geographic area without added thermal metabolic costs.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient 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 score0.163
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.216
Teacher spread0.201 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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