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Record W2025418886 · doi:10.3189/172756400781820859

Ice-age ice-sheet rheology: constraints from the Last Glacial Maximum form of the Laurentide ice sheet

2000· article· en· W2025418886 on OpenAlexafffundabout
W. R. Peltier, D. L. Goldsby, D. L. Kohlstedt, Lev Tarasov

Bibliographic record

VenueAnnals of Glaciology · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space Administration
KeywordsIce sheetGeologyIce streamGlacial periodLast Glacial MaximumIce-sheet modelWisconsin glaciationRheologyGeomorphologyGeophysicsClimatologyCryosphereSea ice

Abstract

fetched live from OpenAlex

Abstract State-of-the-art thermomechanical models of the modern Greenland ice sheet and the ancient Laurentide ice sheet that covered Canada at the Last Glacial Maximum (LGM) are not able to explain simultaneously the observed forms of these cryospheric structures when the same, anisotropy-enhanced, version of the conventional Glen flow law is employed to describe their rheology. The LGM Laurentide ice sheet, predicted to develop in response to orbital climate forcing, is such that the ratio of its thickness to its horizontal extent is extremely large compared to the aspect ratio inferred on the basis of surface-geomorphological and solid-earth-geophysical constraints. We show that if the Glen flow law representation of the rheology is replaced with a new rheology based upon very high quality laboratory measurements of the stress-strain-rate relation then the aspect ratios of both the modern Greenland ice sheet and the Laurentide ice sheet, that existed at the LGM, are simultaneously explained with little or no retuning of the flow law.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.040
GPT teacher head0.279
Teacher spread0.239 · 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

Citations44
Published2000
Admission routes3
Has abstractyes

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