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Record W1984604109 · doi:10.2118/154741-ms

Are You Ready? Developing an Effective Emergency Response Organization

2012· article· en· W1984604109 on OpenAlexaff
Hugh B. Campbell, Ahmed Al Katheeri, Jamal Al Saadi, Christopher Lewis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsEmergency managementEmergency responseSituation awarenessTest (biology)Process (computing)Situational ethicsPlan (archaeology)BusinessOperations managementProcess managementEngineeringKnowledge managementPublic relationsComputer sciencePsychologyPolitical scienceMedical emergencyMedicineGeography

Abstract

fetched live from OpenAlex

Abstract Recent events in the Gulf of Mexico, Australia and Japan bring to focus the importance of having an effective emergency management organization. This paper reviews the process of building an appropriate level of organizational capacity to support an Oil and Gas Operation in times of crisis. Any team providing support during an emergency will benefit from having a structured plan to train, test and enhance its abilities to respond effectively and efficiently during crisis. Total-ABK along with Petrofac Training Services embarked on a 2 year journey to develop and enhance its crisis management capabilities. Efforts began with the re-writing of the affiliate emergency plans to comply with company rules, local regulations and the International Command Structure (ICS). Following the re-write, efforts focused on training staff members for the on-shore emergency crisis team (ERCT) in their specific duties and functions. These staff came from various backgrounds with differing levels of experience. The Company developed a tiered-approach to training the ERCT that began with: Classroom training on the ERP and its contents Structured workshops with the ERCT on the Emergency Response Plan, their specific duties and responsibilities Table top exercises aimed a building confidence for all team members Targeted exercises with escalations in situational complexity Joint scenario workshops with on-shore and offshore participation Communications and media training Call-out exercises to test the emergency hailing system Live field exercises with real-life scenarios The lessons from Total-ABK's efforts include: A structured and tiered approach builds a high degree of confidence in the participants The system of situational escalations pushes participants to anticipate changing conditions and complexity The tiered-training system allows integration of new ERCT members at anytime. An overall sense of confidence in the organization's ability to respond to crisis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0080.005
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.004

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.

Opus teacher head0.101
GPT teacher head0.405
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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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