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Record W2126363109 · doi:10.3189/172756504781830024

On the mechanics of ice-stream shear margins

2004· article· en· W2126363109 on OpenAlexafffund
Christian Schoof

Bibliographic record

VenueJournal of Glaciology · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of British Columbia
FundersKillam Trusts
KeywordsGeologyShear stressShear (geology)MechanicsStream functionFlow (mathematics)Geotechnical engineeringGeometryMathematicsPetrologyPhysics

Abstract

fetched live from OpenAlex

Abstract We investigate the mechanics of ice-stream shear margins based on the assumption that the underlying bed behaves plastically. Sliding is assumed to occur if a prescribed, locally defined yield stress is attained, while no sliding is assumed possible if basal shear stress is lower than the yield stress. Mathematically, the ice-flow problem takes the form of a contact problem, in which the zones of sliding are part of the solution and cannot be prescribed arbitrarily. Simplistic assumptions about the location of till failure, or about mechanical conditions at the bed, predict stress singularities at the margins which lead to corresponding singularities in the basal melt rate. The ice-flow problem is solved using a complex variable method, and an associated quasi-static thermal problem is also solved using a Green’s function. High stress concentrations, which coincide with high rates of strain heating, are found on the ice-stream side of the margins, where basal melting is also greatest. Our results further indicate that a temperate zone may form over time above the bed in the margins. These findings differ from earlier studies based on different sliding laws, suggesting a high sensitivity of margin behaviour to basal conditions.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.223
Teacher spread0.200 · 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 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

Citations75
Published2004
Admission routes2
Has abstractyes

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