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Record W4300991722 · doi:10.5957/icetech-2014-127

Simulation of Managed Sea Ice Loads on a Floating Offshore Platform using GPU-Event Mechanics

2014· article· en· W4300991722 on OpenAlex
Claude Daley, Shadi Alawneh, Dennis Peters, Gary Blades, Bruce Colbourne

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSea iceSubmarine pipelineMarine engineeringGeologyMooringDrift iceWork (physics)Event (particle physics)Seabed gouging by iceArctic ice packEngineeringGeotechnical engineeringMechanical engineeringOceanographyPhysics

Abstract

fetched live from OpenAlex

The paper describes a GPU-based event mechanics (GEM) model of the action of managed pack ice on a floating offshore structure. The ice cover is represented by a large number of discrete polygonal ice floes, of varying thickness. Each ice-structure contact is modeled, as is every ice-ice contact. Time histories of total platform force (net mooring force) and platform position are presented. Ice coverage, floe sizes and thickness are varied in the simulation set. The work represents a further exploration of the possibilities of GEM technology, which was previously used to explore both resistance and local structural loads for ships transiting pack ice. The work is part of a research project at Memorial University of Newfoundland called STePS2 (Sustainable Technology for Polar Ships and Structures).

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.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.831
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.153
GPT teacher head0.420
Teacher spread0.267 · 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

Quick stats

Citations5
Published2014
Admission routes2
Has abstractyes

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