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Record W2247036816

Mechanisms of Ice Softening under High Pressure and Shear

2005· article· en· W2247036816 on OpenAlexvenueno aff
Ian Jordaan, Chuanke Li, Paul Barrette, P. Duval, Jacques Meyssonnier

Bibliographic record

VenueNPARC · 2005
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsSofteningMaterials scienceHydrostatic pressureRecrystallization (geology)Activation energyHydrostatic equilibriumCrystalliteShear (geology)ThermodynamicsComposite materialMetallurgyChemistryGeologyPhysicsPhysical chemistry
DOInot available

Abstract

fetched live from OpenAlex

Under high hydrostatic pressure combined with shear, the structure of polycrystalline ice breaks down into fine-grained material by a process of recrystallization. Accompanying this process is a substantial softening of the material. Measurements of activation energy at low strains prior to the breakdown of structure, at temperatures of -10 °C, show an increase of activation energy from about 80 kJ mol-1 to about 120 kJ mol -1 as the pressure increases, with values at about 70 MPa similar to those found by other researchers for temperatures between -10 and 0 °C. These results suggest that grain boundary effects might result in the increased strain rates. The various energies that might drive the subsequent recrystallization process have been investigated. The results suggest strongly that elastic strain energy is the driving force in the process.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.998

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)0.0030.000

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 head0.015
GPT teacher head0.254
Teacher spread0.238 · 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.

Study designBench or experimental
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

Citations1
Published2005
Admission routes1
Has abstractyes

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