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Record W7010304406

High-speed imaging of mechanisms responsible for sawtooth cyclic loading during ice-crushing

2008· article· en· W7010304406 on OpenAlexaffvenue

Bibliographic record

VenueNPARC · 2008
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsCommunity Sector Council Newfoundland and Labrador
Fundersnot available
KeywordsSpallSawtooth waveFlow (mathematics)Drop (telecommunication)IndentationLoad cell
DOInot available

Abstract

fetched live from OpenAlex

High-speed imaging (30,000 images/s) has been used to observe the behavior of monocrystalline freshwater ice during crushing at -10°C. The ice contact zone consisted of an intact high-pressure central area surrounded by pulverized spall debris and partially refrozen melt. A sawtooth pattern was evident in the load record, similar to that observed in other ice crushing and indentation studies. The spalling behavior that is responsible for any particular load drop in the load record was found to be caused by the presence of small stable fractures in the high-pressure intact ice zone that were created during the preceding spall event and associated load drop. The small fractures are roughly centered in the intact ice region and this explains why the magnitude of the load drops is typically half the prior load value. The removal of ice from the intact ice zone by the viscous radial flow of a thin layer of melt has been described before (Gagnon, R.E. 1994. Melt Layer Thickness Measurements During Crushing Experiments on Freshwater Ice. Journal of Glaciology, 1994, Vol. 40, No. 134, 119-124). This process explains why the small fractures are roughly centered in the high-pressure contact zone. There the flow is stagnant and less ice has melted, relative to the non-central areas, and this leads to high non-uniform stresses in the ice that cause the fractures.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.482

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 head0.016
GPT teacher head0.206
Teacher spread0.191 · 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 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

Citations6
Published2008
Admission routes2
Has abstractyes

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