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Record W4388566292 · doi:10.18280/ijsse.130511

Assessing Occupational Risk: A Classification of Harmful Factors in the Production Environment and Labor Process

2023· article· en· W4388566292 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicEngineering Diagnostics and Reliability
Canadian institutionsnot available
Fundersnot available
KeywordsProduction (economics)Hazardous wasteProcess (computing)Work (physics)Industrial productionMicroclimateBusinessIdentification (biology)Risk analysis (engineering)Occupational safety and healthEnvironmental scienceEnvironmental resource managementEnvironmental economicsEngineeringComputer scienceWaste managementGeography

Abstract

fetched live from OpenAlex

The article presents a draft unified classification of harmful and/or hazardous factors of working conditions for subsequent identification and assessment of any possible occupational risks of employees of various types of economic activity within the framework of the implementation of a risk-based approach in the organization of labor protection at enterprises. In total, 134 harmful and/or dangerous factors present in the production environment and work processes were identified, which were divided into six main groups: physical, chemical, biological, mechanical, psychophysiological, and general industrial pollution. A five-level classification of the main harmful and/or dangerous factors of the production environment and the labor process and their subspecies is proposed. The largest group consists of physical factors, such as industrial noise, vibration, various types of radiation, lighting conditions at enterprises, exposure to electric current and electric arc, the threat of fire or explosion, as well as climate and microclimate conditions and aerosol composition of the air. The conducted research and the creation of a detailed classification of possible harmful and dangerous effects on employees of enterprises formed the basis of a new concept of providing personal protective equipment against harmful factors of production in the Republic of Kazakhstan.

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.

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)0.001
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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