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Record W2008577597 · doi:10.4043/25534-ms

Conical Structures in Ice: Ride-Up, Radius and Results

2015· article· en· W2008577597 on OpenAlexafffund
Anne Barker, Denise Sudom, Mohamed Sayed

Bibliographic record

VenueOTC Arctic Technology Conference · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaGovernment of Canada
KeywordsGeologyWaterlineIce wedgeSea iceConical surfaceRubbleSubmarine pipelinePressure ridgeGeotechnical engineeringIce divideArctic ice packEngineeringDrift iceHullPermafrost

Abstract

fetched live from OpenAlex

Abstract As an ice sheet impinges on the surface of a cone, flexural failure takes place. That ice failure mode causes substantially lower forces than the case of compressive failure, which would take place if ice is to encounter a vertical structure. Previous work by the authors employed a numerical model of ice dynamics in order to predict ice failure patterns and forces on a conical structure. In an initial paper, simulations examined the role of the slope of the cone and the case of ice failure against inverted cones. That study indicated that the slope plays a role on ice loading. Later results, published at ATC 2014, then examined in more detail the roles of structure slope, friction on ice pile-ups and loading on a conical structure and compared the results with the analytical methodology presented in the ISO 19906 Arctic Offshore Structures standard. The present analysis continues to expand the work to examine the role of structure diameter on ice loading and pile-up height. Results are further compared to ISO 19906, with the objective of presenting concrete guidance for the current revision of that standard. Ice forces on upward-breaking cones as a function of structure slope, waterline diameter, ice thickness and ice-structure friction are presented. The results of the study are relevant for structures in ice, such as offshore drilling platforms, bridge piers and offshore wind turbine foundations.

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)0.001
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 score0.275
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.237
Teacher spread0.214 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

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