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Record W2988028990 · doi:10.5194/gmd-13-1763-2020

On the calculation of normalized viscous–plastic sea ice stresses

2020· article· en· W2988028990 on OpenAlexaff
Jean‐François Lemieux, Frédéric Dupont

Bibliographic record

VenueGeoscientific model development · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsGDG EnvironnementEnvironment and Climate Change Canada
Fundersnot available
KeywordsSolverConsistency (knowledge bases)Convergence (economics)Viscous stress tensorStress (linguistics)MathematicsYield (engineering)MechanicsGeologyApplied mathematicsMathematical analysisPhysicsThermodynamicsGeometryCauchy stress tensorMathematical optimization

Abstract

fetched live from OpenAlex

Abstract. Calculating and plotting the normalized states of stress for viscous–plastic sea ice models is a common diagnostic for evaluating the numerical convergence and the physical consistency of a numerical solution. Researchers, however, usually do not explain how they calculate the normalized stresses. Here, we argue that care must be taken when calculating and plotting the normalized states of stress. A physically consistent and numerically converged solution should exhibit normalized stresses that are inside (viscous) or on (plastic) the normalized yield curve. To do so, two possible mistakes need to be avoided. First, when using an implicit solver, normalized stresses should be computed from viscous coefficients and replacement pressure calculated using the previous numerical iterate and the strain rates at the numerator calculated from the latest iterate. Calculating the stresses only from the latest iterate falsely indicates that the solution has numerically converged. Second, for both implicit and explicit (i.e., the EVP) solvers, the stresses should be normalized by the ice strength and not by the replacement pressure. Using the latter, normalized states of stress only lie on the yield curve (i.e., falsely indicating there are no viscous states of stress).

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.714

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 head0.024
GPT teacher head0.204
Teacher spread0.180 · 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 designSimulation or modeling
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

Citations2
Published2020
Admission routes1
Has abstractyes

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