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Record W2969869466 · doi:10.2118/195769-ms

Major Accident Hazards – Own Your Barrier

2019· article· en· W2969869466 on OpenAlex
Gillian Simpson, F. Fitzgerald, Stuart Taylor

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

VenueSPE Offshore Europe Conference and Exhibition · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsHazard analysisWorkgroupIdentification (biology)HazardRisk analysis (engineering)Near missEngineeringWorkforceHazardous wasteOccupational safety and healthForensic engineeringBusinessComputer securityComputer scienceWaste managementMedicinePolitical science

Abstract

fetched live from OpenAlex

Abstract The Health and Safety Executive's analysis shows poor hazard identification and risk analysis is a causal factor in 12 out of 14 recent major hydrocarbon releases, demonstrating that major accidents could be prevented if workers had a better understanding of major accident hazards (MAHs). Therefore, it is proposed that improving awareness of MAHs across the workforce, both onshore and offshore, would lead to better MAH management and a reduction in major accidents. Once the domain of process engineers, major accident hazard management has been largely overlooked by much of industry. It was acknowledged as a problem but ignored in the hope that specialists had it under control. Step Change in Safety's Major Accident Hazard Understanding workgroup responded to this by identifying different job roles (onshore and offshore), evaluating the resources to develop MAH understanding already available and creating a suite of resources to fill the gaps. These resources include an e-learning tool for onshore (office-based) personnel, bowtie lunch and learn sessions, gap analysis tools to identify training requirements of offshore jobs, senior leaders' workshops and a MAH Awareness programme. The MAH Awareness programme, consisting of short films and presentations which can be customised to suit specific worksites and job roles. Each of the four packs explores different aspects of major accident management including MAH identification and analysis, bowties and safety and environmental critical elements, barrier maintenance, assurance and verification and the importance of taking responsibility of ‘owning’ your barrier. Analysis of questionnaires completed before and after exposure to the programme demonstrates that knowledge of MAH management increased by approximately 30%. Additionally, the data demonstrates that elected safety representatives have a greater base knowledge of MAHs than the general offshore workforce, as do technical staff compared to non-technical and those employed by operators compared to contractor employees. Whether this increased knowledge gained through taking part in the MAH Awareness programme is retained or impacts the number of major accidents has not yet been analysed but data such as the number of major accidents, including hydrocarbon releases, will be examined over forthcoming years to evaluate the effectiveness of the resources developed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.995

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.005

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 head0.053
GPT teacher head0.321
Teacher spread0.267 · 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