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Record W2620662059 · doi:10.1002/2017jc012822

Formation of sea ice bridges in narrow straits in response to wind and water stresses

2017· article· en· W2620662059 on OpenAlexaboutno aff
Bhargav Rallabandi, Zhong Zheng, Michael Winton, Howard A. Stone

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersPrinceton University
KeywordsGeologySea iceDrift icePressure ridgeArctic ice packFast iceSea ice thicknessChannel (broadcasting)ArcticSea ice growth processesIce divideGeomorphologyClimatologyOceanographyEngineering

Abstract

fetched live from OpenAlex

Abstract Ice bridges are rigid structures composed of sea ice that form seasonally in the many straits and channels of the Canadian Arctic Archipelago. Driven primarily by atmospheric stresses, these ice bridges are formed when sufficiently thick ice “jams” during the course of its flow between land masses, resulting in a region of stationary compacted ice that is separated from a region of flowing open water (a polynya) by a static arch. Using a continuum description of sea ice that is widely used in climate modeling, we present an asymptotic theory of the process of formation of such bridges in slender channels when the motion of the ice is driven by external wind and water stresses. We show that for an arbitrary channel shape, ice bridges can only form within a range of ice properties that is determined by the channel geometry and the external stress. We then compare the results of our theory with direct numerical simulations and observational evidence. Finally, we provide simple analytical expressions for the mean velocity of the ice flow as a function of the channel shape, the properties of the ice, and the wind and water stresses along the channel.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.073
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.312
Teacher spread0.280 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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