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Record W2069439440 · doi:10.3189/172756503781830935

Statistical behaviour of the deformation for first loading of polycrystalline ice

2003· article· en· W2069439440 on OpenAlexaff
L. W. Gold

Bibliographic record

VenueJournal of Glaciology · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsCommunity Sector Council Newfoundland and Labrador
Fundersnot available
KeywordsLog-normal distributionProbability density functionMaterials sciencePerpendicularDeformation (meteorology)PopulationFracture (geology)CrystalliteDislocationGeologyComposite materialGeometryMathematicsStatisticsMetallurgy

Abstract

fetched live from OpenAlex

Abstract A statistical analysis of the lengths of grain-boundary and transgranular cracks induced during the initial straining of columnar-grain ice by a compressive load applied perpendicular to the long direction of the columns is presented. The analysis shows that the crack lengths are randomly distributed and form distinct but correlated populations.The lognormal distribution function is shown to be a good descriptor of the populations for 5–90% of their range. Statistical models are presented for the lognormal behaviour of the crack-length distribution and for the strain dependence of the crack density. The models assume that a change in the value of the random variable of the respective population depends on the population value of the variable at the time of the change. It is shown that the model for the strain dependence of the crack density is suitable for the strain dependence of the acoustic emission, measured in both columnar-grain and granular ice subject to constant compressive loads. Evidence is also presented for a lognormal dependence of the dislocation density on strain. The analysis demonstrates that the cracks that form during the initial straining of polycrystalline ice are independent, random events and that the resulting crack populations are precursors to failure by fracture.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.244

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)0.0000.000

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 head0.022
GPT teacher head0.240
Teacher spread0.218 · 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 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

Citations2
Published2003
Admission routes1
Has abstractyes

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