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Record W2811481274 · doi:10.2495/safe-v8-n3-451-462

A novel fault tree analysis approach to investigate uncommon accidents in quarries: a case study

2018· article· en· W2811481274 on OpenAlexvenueno aff
Dario Lippiello, Guido Alfaro Degan, Mario Pinzari

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFault tree analysisTree (set theory)Forensic engineeringComputer scienceEngineeringReliability engineeringMathematics

Abstract

fetched live from OpenAlex

Quarrying companies are increasingly involved in developing effective Occupational Health and Safety Management Systems (OHSMS) to protect both their own staff and all those collaborating with their organization. The adoption of schemes such as BS OHSAS 18001:2007 helps the company to comply with legal requirements and serves the personnel both by raising awareness of the potential adverse consequences to which they are exposed (i.e., the risk of accident and potential occupational illness), and by limiting and managing the risk of occupational hazards.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.047
GPT teacher head0.351
Teacher spread0.304 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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