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Record W2045054786 · doi:10.1504/ijram.2007.014090

Risk management programme for occupational safety and health in surface mining operations

2007· article· en· W2045054786 on OpenAlexaff
Dragan Komljenović, Vladislav Kecojević

Bibliographic record

VenueInternational Journal of Risk Assessment and Management · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversité du Québec à Trois-RivièresHydro-Québec
Fundersnot available
KeywordsRisk managementRisk analysis (engineering)Risk assessmentOccupational safety and healthIT risk managementProcess (computing)Risk management planEngineeringBusinessComputer scienceMedicineComputer security

Abstract

fetched live from OpenAlex

This paper describes a systematic risk analysis process for occupational safety and health (OSH). A concept of technological risk management and risk assessment is applied. A review of published risk management and assessment applications for various industries is presented and a generalised approach to risk management for OSH in surface mining is proposed. The approach consists of six phases. This process can be used to help judge the tolerability of risk and aid in choosing between potential risk reduction and/or risk avoidance measures. The paper also underlines the benefits of applying risk management concepts from the decision maker's perspective.

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)0.044
metaresearch head score (Gemma)0.049
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: none
Teacher disagreement score0.044
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.049
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.002
Science and technology studies0.0030.002
Scholarly communication0.0030.003
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.088
GPT teacher head0.527
Teacher spread0.440 · 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

Citations9
Published2007
Admission routes1
Has abstractyes

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