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Record W3043647929 · doi:10.1002/prs.12177

A framework for the risk assessment of residual hazardous material in the dynamic environment of a composite production process considering operational time variation

2020· article· en· W3043647929 on OpenAlexaff
Alireza Asgari, Muke Kibala, Yvan Beauregard

Bibliographic record

VenueProcess Safety Progress · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsHazardous wasteRisk analysis (engineering)Process (computing)Production (economics)Risk assessmentResidual riskContext (archaeology)ResidualReliability engineeringOperational riskDynamic assessmentProcess safetyProduct (mathematics)EngineeringWork in processComputer scienceRisk managementOperations managementBusinessWaste managementComputer security

Abstract

fetched live from OpenAlex

Abstract In the chemical product production process, activities relating to the transportation and storage of residual hazardous materials increase the risk of workplace contamination and associated negative impacts on worker health and safety. In this context, the dynamic nature of the process complicates the study of the parameters underlying the related operational risks. The current operational risks assessment standard, ISO 31000, cannot provide a practical pathway to assess risks in dynamic operational environments. We, therefore, model a dynamic environment to investigate the effect of functional time variation on risk assessment. Verification of the claimed effect will show that even with rigorous instruction when using the available equipment, the process cannot fully conform to current safety standards.

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)0.006
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.366
Teacher spread0.327 · 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 designSimulation or modeling
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

Citations4
Published2020
Admission routes1
Has abstractyes

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