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Record W2786647801 · doi:10.1126/science.aan8677

High-energy, high-fat lifestyle challenges an Arctic apex predator, the polar bear

2018· article· en· W2786647801 on OpenAlexaff
Anthony M. Pagano, George M. Durner, Karyn D. Rode, Todd C. Atwood, Shannon Atkinson, EE Peacock, Daniel P. Costa, Megan A. Owen, Terrie M. Williams

Bibliographic record

VenueScience · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsResearch Manitoba
FundersNorth Pacific Research BoardU.S. Geological SurveyInternational Association for Bear Research and ManagementWashington State UniversityUniversity of California, Santa CruzSeaWorld and Busch Gardens Conservation FundWorld Wildlife FundNational Science Foundation
KeywordsUrsus maritimusSea iceForagingApex predatorArcticPredationPolarBiologyMetabolic rateArctic ice packEnvironmental sciencePredatorEcologyFragmentation (computing)Polar nightOceanographyGeologyPhysics

Abstract

fetched live from OpenAlex

) populations have been attributed to changing sea ice conditions, but with limited information on the causative mechanisms. By simultaneously measuring field metabolic rates, daily activity patterns, body condition, and foraging success of polar bears moving on the spring sea ice, we found that high metabolic rates (1.6 times greater than previously assumed) coupled with low intake of fat-rich marine mammal prey resulted in an energy deficit for more than half of the bears examined. Activity and movement on the sea ice strongly influenced metabolic demands. Consequently, increases in mobility resulting from ongoing and forecasted declines in and fragmentation of sea ice are likely to increase energy demands and may be an important factor explaining observed declines in body condition and survival.

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 score0.003
Threshold uncertainty score0.006

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.013
GPT teacher head0.213
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 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

Citations168
Published2018
Admission routes1
Has abstractyes

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