MétaCan
Menu
Back to cohort
Record W2148041989 · doi:10.1111/mms.12097

Thermal limits in young northern fur seals, <i>Callorhinus ursinus</i>

2014· article· en· W2148041989 on OpenAlexaff
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
Fundersnot available
KeywordsForagingEcologyPredationFur sealEnvironmental scienceBiologyClimate changeOceanographyZoologyGeology

Abstract

fetched live from OpenAlex

Abstract The thermoregulatory abilities of northern fur seals (Callorhinus ursinus) during their first two years in the frigid waters of the North Pacific Ocean may limit their geographic distribution and alter the costs for exploiting different species of prey. We determined the thermoneutral zone of six young northern fur seals by measuring their metabolism in ambient air and controlled water temperatures (0°C–12°C) from ages 8 to 24 mo. We found that the ambient air temperatures within our study (overall 1.5°C–23.9°C) did not affect resting metabolic rates. Calculated lower critical temperatures in water varied between 3.9°C and 8.0°C, while an upper critical temperature in water was only discernible during a single set of trials. These thermal responses provide insight into the possible physiological constraints on foraging ecology in young northern fur seals, as well as the potential energetic consequences of ocean climate change and altered prey distributions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueMarine Mammal ScienceSame topicMarine animal studies overviewFrench-language works237,207