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Record W2074228691 · doi:10.4236/ojsst.2014.41007

Introduction of Innovative Equipment in Mining: Impact on Occupational Health and Safety

2014· article· en· W2074228691 on OpenAlexafffund
Bryan Boudreau-Trudel, Sylvie Nadeau, Kazimierz Zaraś, Isabelle Deschamps

Bibliographic record

VenueOpen Journal of Safety Science and Technology · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsÉcole de Technologie SupérieurePolytechnique MontréalUniversité du Québec en Abitibi-Témiscamingue
FundersFonds de recherche du Québec – Nature et technologiesFondation de l’Université du Québec en Abitibi-TémiscamingueÉcole de technologie supérieure
KeywordsOccupational safety and healthRisk analysis (engineering)Work (physics)Personal protective equipmentOccupational injuryInjury preventionHuman factors and ergonomicsPoison controlForensic engineeringEnvironmental healthBusinessEngineeringMedicine

Abstract

fetched live from OpenAlex

Occupational health and safety in mining has clearly improved in developed countries over the past twenty years, but accidents and illness still occur with unacceptable frequency. The arrival of new mining equipment, bigger, more powerful and complex and requiring a higher skill level appears also to increase certain specific risks of accident and work-related illness. The objective of this paper is to examine the impact of new equipment on occupational health and safety in underground mining. The injury rate associated with eight equipment introduction projects was examined. The results show clearly that the introduction of new equipment with technological innovations does not automatically reduce the injury rate. The new equipment may even generate a higher injury rate than the equipment it replaced. Ergonomic deficiencies were noted in some of the new equipment. We suggest that future research focus on identifying the mechanisms and conditions that determine injury rate following the acquisition of innovative as means of improving occupational health and safety in mining. Successful implementation of new mining equipment appears to depend on the specific conditions of use.

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)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.064
GPT teacher head0.488
Teacher spread0.424 · 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 designObservational
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

Citations12
Published2014
Admission routes2
Has abstractyes

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