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Record W4299735909 · doi:10.5957/icetech-2012-125

Response of IACS URI Ship Structures to Real-time Full-scale Operational Ice Loads Bruce Quinton

2012· article· en· W4299735909 on OpenAlexaff
Bruce Quinton, Claude Daley, Robert Gagnon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsNational Research Council CanadaMemorial University of Newfoundland
Fundersnot available
KeywordsFraming (construction)Structural engineeringFinite element methodEngineeringDeflection (physics)GeologyPhysicsOptics

Abstract

fetched live from OpenAlex

Moving ice loads can incite significantly different structural responses in a steel grillage structure than can stationary ice loads. This is significant because the accepted standard for the design and analysis of ice-classed ship structures is to assume a stationary ice load (IACS URI I2.3.1). The following work utilizes the 4D Pressure Method ((Quinton, Daley, and Gagnon 2012)) to apply thirty-five of the most significant ice loads recorded during the USCGC Polar Sea trials (1982-86), to fourteen IACS URI PC1-7 classed grillages; using explicit finite element analyses. Two grillage variations for each of the seven PC classes were examined: grillages with "built T" framing and grillages with "flatbar" framing. In short, the following simulations directly employ real-time/real-space measured full-scale ice loads, and thus provide insight into the structural capabilities of the various IACS URI polar classes when subject to actual (moving) ice loads.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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 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

Citations0
Published2012
Admission routes1
Has abstractyes

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