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Record W2096536349 · doi:10.2193/2007-123

Quantitative Support for a Subjective Fatness Index for Immobilized Polar Bears

2008· article· en· W2096536349 on OpenAlexaff
Ian Stirling, Gregory W. Thiemann, Evan S. Richardson

Bibliographic record

VenueJournal of Wildlife Management · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsDalhousie UniversityUniversity of Alberta
Fundersnot available
KeywordsUrsus maritimusPolarIndex (typography)Beaufort seaRating scaleBody mass indexBayBiologyScale (ratio)DemographyEcologyPsychologyGeographyEndocrinologyDevelopmental psychologyComputer scienceCartography

Abstract

fetched live from OpenAlex

ABSTRACT Adequate stores of body fat are essential for survival and reproduction of polar bears ( Ursus maritimus ). However, polar bear body fat levels can be difficult to quantify in the field. For >30 years, biologists have subjectively estimated relative fatness of immobilized polar bears by assigning individuals a rating from 1 to 5, with 1 being leanest and 5 most obese. Although previous studies suggested this fatness index (FI) rating accurately reflects large‐scale differences in body condition, its relationship to more quantitative measures of condition has not been explored. We compared the FI rating of individual polar bears in western Hudson Bay and the Beaufort Sea to 2 quantitative measures of body condition: the Quetelet Index (ratio of mass to length 2 ) and the relative lipid content of adipose tissue. We found a significant relationship between FI rating and both Quetelet Index values and adipose lipid content. Our data demonstrate that the FI rating accurately reflects overall body condition, regardless of polar bear age, sex, or nutritional phase. We suggest that continued field use of the FI rating could provide valuable information on ecological effects of large‐scale environmental change on polar bear populations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.032
GPT teacher head0.284
Teacher spread0.252 · 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 designNot applicable
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

Citations79
Published2008
Admission routes1
Has abstractyes

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