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Record W7011500393

Mechanics of Ice Compressive Failure, Probabilistic Averaging and Design Load Estimation

2006· article· en· W7011500393 on OpenAlexfundvenueno aff

Bibliographic record

VenueNPARC · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsScale (ratio)Probabilistic logicSubmarine pipelineViscoelasticityCompressive strengthVolume (thermodynamics)Pressure measurement
DOInot available

Abstract

fetched live from OpenAlex

Compressive ice failure is an important aspect in the design of offshore structures in ice environments. The authors concentrate attention on the crushing failure mode. The scale effect is a phenomenon whereby the average pressure decreases with contact area. In classical elasticity, viscoelasticity and plasticity, there is no scale effect. In the case of ice compressive failure, there is ample evidence of a scale effect. Two factors are dominant in this situation. Ice is prone to fracture, thus reducing the volume of material to be crushed, and aiding in the formation of high pressure zones. The second factor in the scale effect is the averaging process that results from the transition from local pressures on small areas (dominated by high pressure zones) to global pressures, where this effect is averaged. The analysis of probabilistic averaging is described with the example of pressures measured on the Molikpaq structure. The implications for design are significant.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.196
Teacher spread0.166 · 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 designSimulation or modeling
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

Citations4
Published2006
Admission routes2
Has abstractyes

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