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Record W2941540825 · doi:10.1029/2018jc014898

Leads and Associated Sea Ice Drift in the Beaufort Sea in Winter

2019· article· en· W2941540825 on OpenAlexaboutno aff
Benjamin J. Lewis, Jennifer Hutchings

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNational Aeronautics and Space Administration
KeywordsBeaufort scaleGeologyOcean gyreBeaufort seaAnticycloneSea iceOceanographyArctic ice packClimatologyFast iceDrift iceSeasonalityFishery

Abstract

fetched live from OpenAlex

Abstract Beaufort Sea ice motion is episodic in winter, on average following the anticyclonic motion of the Beaufort Gyre. Weather systems cause the ice pack to fracture in characteristic patterns that depend on the location and trajectory of the weather system in relation to the coast. The majority of leads associated with anticyclonic motion in the Beaufort Sea are coastal leads that form perpendicular from promontories along the coast and landfast ice edge. Between 40% and 90% (depending on location along the Beaufort coast) winter sea ice motion is associated with leads, including coastal leads, coastal flaw leads, and interior lead patterns. In winter much of the Beaufort Gyre ice drift results from ice‐coast interaction, with high ice drift localized on the leeward side of fractures that propagate from the coast. In the vicinity of Point Barrow, which has the largest occurrence of coastal leads, ice drift rates are enhanced due to this ice‐coast interaction. No trends in occurrence of these leads are found over the 20 winters from December 1993 to May 2013. Seasonality in the occurrence of the leads follows seasonality in the location of the Beaufort High. In order for ice to be transported from the northeast part of the Beaufort Gyre to the southwest in a single winter, anticyclones would need to break their geospatial climatic norm, needing a higher prevalence of anticyclones in the eastern Beaufort early in the season and higher prevalence in the west Canada Basin later in the season.

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)0.003
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 score0.011
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.021
GPT teacher head0.288
Teacher spread0.267 · 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

Citations50
Published2019
Admission routes1
Has abstractyes

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