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Record W1983293535 · doi:10.4043/23761-ms

Approaches for refining pack ice driving force estimates for the Beaufort Sea

2012· article· en· W1983293535 on OpenAlexaff
Anne Barker, G.W. Timco

Bibliographic record

VenueOTC Arctic Technology Conference · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSubmarine pipelineMarine engineeringSea iceArcticEnvironmental scienceRefining (metallurgy)Range (aeronautics)Arctic ice packGeologyComputer scienceEngineeringClimatologyOceanographyAerospace engineering

Abstract

fetched live from OpenAlex

Abstract There are large oil and gas resources in the shear zone region of theBeaufort Sea. Development of these resources would entail many factorsfor consideration. One of the most important is the ice load on drillingand production platforms. However, there is very large uncertainty on theice loads in this region due to a clear lack of knowledge of the pack icedriving forces. This pack ice driving force is one of the primarymechanisms that dictate how much force the ice can exert on an offshoreplatform, even if large multi-year ice floes are present. Improvedknowledge of this force would significantly reduce uncertainty in the designice loads, provide essential baseline engineering knowledge and a more reliablestructure, leading to greater regulatory certainty and safer and moreeconomical offshore operations. The pack ice driving force, as a function of width, can be calculatedthrough an equation in the ISO Arctic Offshore Structures Standard. However, a relevant parameter is still poorly defined for this equationand spans a large range. As a result, calculations of driving forces arevery uncertain, yet it is the key limiting force mechanism for the BeaufortSea. This paper presents the results of a study that investigated meansof refining uncertainty when calculating pack ice driving forces. Anoverview of the standard method of determining these forces is given, as wellas a discussion of the historical development of, and implications ofuncertainty in, calculating the pack ice force. Methods for refining theuncertainty are presented and comparisons are discussed. Numericalmodelling studies offer the greatest potential for refining the uncertaintybased on cost, usefulness, confidence in the results and studyopportunities. The information provided in this paper has applicationsfor refinement of pack ice driving force calculations in current engineeringstandards. A clear understanding of the magnitude of pack ice drivingforces would help to reduce the risk of failure of engineering structures andimprove their safety, by enabling a significantly better definition of theanticipated ice loads and the upper limit of the loads for the BeaufortSea.

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)0.007
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 score0.066
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.045
GPT teacher head0.241
Teacher spread0.196 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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