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Record W2341980949 · doi:10.1002/2016jc011638

A dynamical model of Kara Sea land‐fast ice

2016· article· en· W2341980949 on OpenAlexfundno aff
Einar Ólason

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersDeutsche ForschungsgemeinschaftAlberta Agricultural Research InstituteUniversity Corporation for Atmospheric ResearchNational Center for Atmospheric Research
KeywordsSea iceSolverForcing (mathematics)GeologyShoreMomentum (technical analysis)ClimatologyMeteorologyMathematicsOceanographyPhysicsMathematical optimization

Abstract

fetched live from OpenAlex

Abstract This paper introduces modifications to the traditional viscous‐plastic sea‐ice dynamical model, which are necessary to model land‐fast ice in the Kara Sea in a realistic manner. The most important modifications are an increase in the maximum viscosity from the standard value of to , and to use a solver for the momentum equation capable of correctly solving for small ice velocities (the limit here is set to m/s). Given these modifications, a necessary condition for a realistic fast‐ice simulation is that the yield curve give sufficient uniaxial compressive strength. This is consistent with the idea that land‐fast ice in the Kara Sea forms primarily via static arching. The modified model is tested and tuned using forcing data and observations from 1997 and 1998. The results show that it is possible to model land‐fast ice using this model with the modifications mentioned above. The model performs well in terms of modeled fast‐ice extent, but suffers from unrealistic break‐ups during the start and end of the fast‐ice season. The main results are that fast ice in the Kara Sea is supported by arching of the ice, the arches footers resting on a chain of islands off shore.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.386

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 integrity0.0000.001
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 head0.033
GPT teacher head0.292
Teacher spread0.258 · 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

Citations34
Published2016
Admission routes1
Has abstractyes

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