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Record W6910186449 · doi:10.4224/12340977

Experimental investigation of ice rubble behaviour and strength in punch tests

2000· report· en· W6910186449 on OpenAlexvenueaboutno aff

Bibliographic record

VenueNPARC · 2000
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRubbleStrength of materialsLaboratory testRange (aeronautics)Plasticity

Abstract

fetched live from OpenAlex

The report summarises the results from three series of plunge tests on ice rubble conducted at the University of Calgary, and interpretation of these results. The tests were conducted as two-dimensional plane strain experiments in a transparent tank that permitted video recording of the rubble behaviour during the tests. The majority of the tests were conducted using standard fresh-water ice cubes manufactured using commercial ice making machines, while the most recent tests were conducted using larger ice pieces. The observations of the behaviour of the rubble during the tests suggested that there was a significant difference in behaviour observed at low and high loading speeds. At low speeds, well-defined failure planes were observed, and, for unconsolidated rubble, the behaviour could be described as purely frictional. As the ice was permitted to consolidate, the addition of local regelation altered the value of the angle of friction, but the observed behaviour was little changed. Over the range of low speed tests ( to 40 mm/sec) there was no appreciable effect of speed on friction angle. However, there is some effect of rubble thickness. At high speeds of loading, no clearly defined failure planes were observed. The failure could be described as gradual, with the disturbance of the ice rubble taking place within the mass of the rubble. The analogy used for the interpretation of the strength of the rubble (plate pull-out in granular materials) was not applicable to these tests. The analysis of these results, and the extension of the test programme for the larger ice pieces, is on-going.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.041
GPT teacher head0.313
Teacher spread0.272 · 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 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

Citations3
Published2000
Admission routes2
Has abstractyes

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