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Record W2014213340 · doi:10.4043/24548-ms

An Implementation of ISO 19906 Formulae for Global Sea Ice Loads within a Probabilistic Framework

2014· article· en· W2014213340 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 · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsCentre For Cold Ocean Resources Engineering
Fundersnot available
KeywordsSea iceScalingRandomnessScale (ratio)Computer scienceGeologyMathematicsStatisticsClimatologyGeometry

Abstract

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Abstract The ISO 19906 standard provides guidance for the calculation of characteristic ice loads on offshore structures in arctic and cold regions. Ice failure is a complex process and the development and improvement of ice load models can be challenging, in large part because of difficulties obtaining full-scale data and scaling issues when extrapolating small-scale test data. Many of the ice load models referenced in ISO 19906 were developed during arctic exploration in the 70's and 80's. Typically, simplified geometries are assumed for both the structure and ice features in order to obtain analytic solutions; other simplifications may be incorporated appropriate for the specific applications considered and information available. A significant proportion of referenced models provide the maximum load during an interaction, rather than the development of the load over time. This can be a limitation were penetration into a thick ridge is limited by available driving force and kinetic energy. Given the large variety of ice conditions to which a structure may be subjected and the apparent randomness in ice fracture and damage mechanisms, there can be considerable variation in loads. Ice strength may be set to a characteristic fixed value, the ISO model for global sea ice loads is based on a relationship that considers ice thickness and contact width and is based on upper envelop fits to failure data. When determining the appropriate characteristic load on a structure, consideration should be given to exposure (i.e., the number and durations of ice interactions). Loads based on characteristic values for parameters such as ice thickness and ice strength could be inaccurate for scenarios where the exposure is significantly different than that on which the characteristic values were based. The application of probabilistic methods can be used to account for differences in exposure. While ISO 19906 references such methods, guidelines on implementation is limited. This paper examines issues in implementing available formulae for ice loads on fixed structures within a probabilistic framework and shows how characteristic ice loads differ depending on the model and assumptions used. The Sea Ice Loads Software (SILS), a probabilistic framework developed by C-CORE for calculating characteristic ice loads using the methods referenced in ISO19906, is used for the analyses and comparisons.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.762

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 science0.0010.000
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 head0.011
GPT teacher head0.267
Teacher spread0.256 · 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