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Record W2072031377 · doi:10.4043/25520-ms

Update on Probabilistic Assessment of Multi-year Sea Ice Loads on Vertical-faced Structures

2015· article· en· W2072031377 on OpenAlex

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.

Bibliographic record

VenueOTC Arctic Technology Conference · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsConocoPhillips (Canada)Centre For Cold Ocean Resources Engineering
Fundersnot available
KeywordsRidgeMonte Carlo methodSea iceProbabilistic logicComputer scienceSoftwareBreakoutMarine engineeringGeologyEngineeringClimatologyMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract The Sea Ice Loads Software (SILS) is a Monte-Carlo type simulator developed by C-CORE for determining first and multiyear design sea ice loads, following the ISO 19906 methodology. Due to complexities in ice failure mechanics and associated uncertainties, these models are necessarily empirical or require simplifying assumptions. To account for uncertainty, conservatism must be built into models. A number of improvements to the software have recently been implemented in order to more realistically include a number of model components applicable to multi-year ridge loads and limit forces. This paper provides an overview of new modules accounting for ridge breakout, driving force ramp-up and ridge geometry modeling. Sensitivity runs show that design loads are affected significantly by accounting for these processes, compared to the previous implementation of ISO 19906 in SILS. The objective of this paper is to present the analytical model components and their implementation in SILS, and to demonstrate the influence of the changes by means of a scenario wherein a vertical sided structure encounters multi-year level ice and ridges.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.268
Teacher spread0.239 · 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