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Record W4300627491 · doi:10.5957/icetech-2012-144

Arctic Offshore Structure EER Risk Based Standards and Methods of Risk Analysis

2012· article· en· W4300627491 on OpenAlexaboutno aff
James P. Poplin, Frank G. Bercha

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear decommissioningReliability (semiconductor)Risk analysis (engineering)Risk assessmentComputer scienceReliability engineeringSubmarine pipelineEngineeringComputer securityBusiness

Abstract

fetched live from OpenAlex

The Canadian Offshore Structure Performance Based EER Standards 2006 and International Standard ISO 19906 Arctic Offshore Structures 2010 were developed to help ensure that offshore structures deployed where arctic conditions prevail, provide the appropriate level of reliability with respect to personnel safety, environmental protection and asset value. Both standards address escape, evacuation and rescue (EER). The Canadian standard provides reliability targets for key elements and the totality of the EER process whereas the ISO standard addresses design, construction, transportation, installation and decommissioning phases of the structure. EER is a system that mitigates the effects of major accident hazards to personnel. A suite of risk analyses methodologies are typically employed to assess the EER philosophy and to confirm the provisions of the overall EER system design. The objectives of such analyses are to assess the design adequacy (from an EER perspective) at key stages in the design, to assess the impact of changes to the design that are proposed and to demonstrate that risks to personnel in the overall design are as low as reasonably practicable (ALARP). Following a general description of the Canadian and ISO standard’s EER risk and reliability based provisions, this paper provides an overview of some of the applicable risk methodologies including the EER analysis.

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)0.001
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 score0.265
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.005
GPT teacher head0.252
Teacher spread0.247 · 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
Published2012
Admission routes1
Has abstractyes

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