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Record W2069053908 · doi:10.14430/arctic410

Movements of Walruses Radio-tagged in Bristol Bay, Alaska

2010· article· en· W2069053908 on OpenAlexvenueno aff
Chadwick V. Jay, Sue Hills

Bibliographic record

VenueARCTIC · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBaySpring (device)ForagingGeographyOceanographyRange (aeronautics)FisheryEnvironmental scienceEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Satellite radio-location data from 57 adult male Pacific walruses (Odobenus rosmarus divergens) were used to estimate haul-out fidelity, broadly describe seasonal foraging distributions, and determine the approximate timing of autumn migration from Bristol Bay, Alaska. Data were collected intermittently during 1987–91 and 1995–2000, primarily during the period from May to October. Transmitter longevity ranged from less than 1 day to 560 days (median 75 d). The four tagging sites were the only haul-outs that were commonly used in the bay from spring through autumn. Mean fidelity, defined as the chance that an animal will return to an area where it previously hauled out, was 0.56 (SE = 0.09). However, small sample sizes precluded comparisons of fidelity among years and among haul-outs by season. No tagged animals migrated out of the bay between spring and early autumn. Combined monthly locations suggest that foraging occurred primarily in the southern and eastern areas of the bay in spring and gradually shifted towards northwestern areas in late autumn and winter. Ninety-eight percent of the in-water locations were in waters under 60 m deep, which account for 76% of the study area. Some animals migrated out of the bay in late autumn and winter; others remained within the bay throughout the year. Those making long-range migrations departed the bay during November and December.

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 categoriesInsufficient 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 score0.007
Threshold uncertainty score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Citations23
Published2010
Admission routes1
Has abstractyes

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Same venueARCTICSame topicArctic and Antarctic ice dynamicsFrench-language works237,207