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Record W2039572662 · doi:10.4043/25535-ms

Analysis of a Large Ice Shear Wall: Implications for Pack Ice Driving Forces

2015· article· en· W2039572662 on OpenAlexafffund
G.W. Timco, Anne Barker

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 CanadaEnergy Council of Canada
KeywordsBeaufort seaRubbleGeologySea iceSeabedSubmarine pipelineShear forceShearing (physics)Arctic ice packGeotechnical engineeringGeodesyEngineeringStructural engineeringClimatologyOceanography

Abstract

fetched live from OpenAlex

Abstract A simple analysis is presented to examine the forces necessary to create a large shear wall of ice which was observed in the Beaufort Sea. This shear wall was approximately 130 m long and 23 m high (from the seabed). Two types of analysis are presented. First, a distinct feature of a 0.8 m thick ice floe which had been pushed up the 8 m high (above the ice level) rubble pile was analyzed by considering a number of analytical models for ice ride-up. The analysis showed that the line loads were estimated to be on the order of 65 to 150 kN/m, and 90 to 208 kN/m for friction values of 0.3 and 0.5 respectively. A second analysis of the creation of the shear wall by a large-scale shearing event suggested the global force would have been on the order of 42 to 90 MN. These values are in agreement with measurements of global ice loads on offshore caisson structures. The analysis was extended to estimate the pack ice driving force for this event. Although a number of gross assumptions were made, the calculated values are in good agreement with other previous estimates of the pack ice driving force in the Beaufort Sea. Overall this study has indicated that observations of these large ice features and subsequent analysis can be a very useful method for gaining additional insight on pack ice driving forces.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.264
Teacher spread0.240 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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