Assessing Occupational Risk: A Classification of Harmful Factors in the Production Environment and Labor Process
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".