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Record W2321753790 · doi:10.1139/z2012-033

Long-distance swimming by polar bears (<i>Ursus maritimus</i>) of the southern Beaufort Sea during years of extensive open water

2012· article· en· W2321753790 on OpenAlexvenueno aff
Anthony M. Pagano, George M. Durner, Steven C. Amstrup, Kristin S. Simac, Geoffrey S. York

Bibliographic record

VenueCanadian Journal of Zoology · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsUrsus maritimusSea iceOpen waterBeaufort scalePredationForagingBiologyPopulationFast iceBeaufort seaArctic ice packMarine mammalOceanographyPolarFisheryEcologyAntarctic sea iceGeologyDemography

Abstract

fetched live from OpenAlex

Polar bears ( Ursus maritimus Phipps, 1774) depend on sea ice for catching marine mammal prey. Recent sea-ice declines have been linked to reductions in body condition, survival, and population size. Reduced foraging opportunity is hypothesized to be the primary cause of sea-ice-linked declines, but the costs of travel through a deteriorated sea-ice environment also may be a factor. We used movement data from 52 adult female polar bears wearing Global Positioning System (GPS) collars, including some with dependent young, to document long-distance swimming (&gt;50 km) by polar bears in the southern Beaufort and Chukchi seas. During 6 years (2004–2009), we identified 50 long-distance swims by 20 bears. Swim duration and distance ranged from 0.7 to 9.7 days (mean = 3.4 days) and 53.7 to 687.1 km (mean = 154.2 km), respectively. Frequency of swimming appeared to increase over the course of the study. We show that adult female polar bears and their cubs are capable of swimming long distances during periods when extensive areas of open water are present. However, long-distance swimming appears to have higher energetic demands than moving over sea ice. Our observations suggest long-distance swimming is a behavioral response to declining summer sea-ice conditions.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.960

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)0.0010.000

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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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

Citations100
Published2012
Admission routes1
Has abstractyes

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