Arctic Offshore Structure EER Risk Based Standards and Methods of Risk Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".