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Record W2247554850

Modelling Rubble Field Development at Isserk I-15 and Its Implications for Engineering Ice Rubble

2007· article· en· W2247554850 on OpenAlexvenueaboutno aff
Anne Barker, G.W. Timco

Bibliographic record

VenueNPARC · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRubbleCaissonGeologySubmarine pipelineStormGeotechnical engineeringOceanography
DOInot available

Abstract

fetched live from OpenAlex

A grounded rubble field can be advantageous when it surrounds an Arctic offshore structure. It can attenuate ice loads and could be used as a base for an evacuation shelter. These advantages mean that the creation of a sTable., grounded rubble field at an offshore site can be beneficial for locations in the transition and landfast ice zones of the Beaufort Sea. In the winter of 1989~1990, the Molikpaq Caisson was deployed at the Isserk I-15 site in the Canadian Beaufort Sea. During October and early November, the Molikpaq was exposed to mobile first year ice driven by strong winds. This series of storms built a “cigar-shaped” rubble field at the site. Detailed information concerning the rubble field development at this site was collected. In this paper, a numerical model is used to examine the factors that affect the generation of a grounded rubble field. The observations from Isserk are used to validate the model. By investigating several scenarios of ice interaction, through an examination of the influence of water depth, ice thickness, velocity and fetch length upon rubble extent, the model can be used to provide information on the rate of growth of grounded rubble fields. The data can be used to quantify the effects of ice rubble on reducing ice loads in the winter and provide guidance on rubble stability and optimal rubble field extent, as well as evacuation options.

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)0.000
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: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.218
Teacher spread0.199 · 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 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

Citations5
Published2007
Admission routes2
Has abstractyes

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