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Record W2942616305 · doi:10.1029/2019gl082526

Regularized Coulomb Friction Laws for Ice Sheet Sliding: Application to Pine Island Glacier, Antarctica

2019· article· en· W2942616305 on OpenAlexafffund
Ian Joughin, B. E. Smith, Christian Schoof

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of British Columbia
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space Administration
KeywordsGlacierGeologyAntarctic ice sheetLawIce sheetPower lawMechanicsPhysicsOceanographyGeomorphologySea iceMathematics

Abstract

fetched live from OpenAlex

The choice of the best basal friction law to use in ice-sheet models remains a source of uncertainty in projections of sea level. The parameters in commonly used friction laws can produce a broad range of behavior and are poorly constrained. Here we use a time series of elevation and speed data to examine the simulated transient response of Pine Island Glacier, Antarctica, to a loss of basal traction as its grounding line retreats. We evaluate a variety of friction laws, which produces a diversity of responses, to determine which best reproduces the observed speedup when forced with the observed thinning. Forms of the commonly used power law friction provide much larger model-data disagreement than less commonly used regularized Coulomb friction in which cavitation effects yield an upper bound on basal friction. Thus, adoption of such friction laws could substantially improve the fidelity of large-scale simulations to determine future sea level.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.085
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.031
GPT teacher head0.293
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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

Citations271
Published2019
Admission routes2
Has abstractyes

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