Provision of Personal Protection Equipment, According to the Risk of Exposure to Harmful Industrial Factors during Copper-Polymetallic Ore Mining
Bibliographic record
Abstract
The article addresses the issue of providing personal protective equipment, taking into account the risk of exposure to harmful factors of production during the extraction of copper ore in ore deposits located at great depths, i.e., underground mining.The working conditions of miners are characterized by a complex of harmful production factors, which include, above all, high dust and gas pollution of the air and a heated microclimate.The investigation revealed that workers in underground mines are exposed to the harmful effects of chemical factors.The evaluation of working conditions based on microclimatic factors showed an excess of air temperature (4-12 times) and relative humidity (3-10 times) in the workplace.Mathematical data processing has shown that the distribution of dangerous and harmful factors is subject to polynomial dependence.Mathematical data processing showed that the distribution of hazardous and harmful factors is subject to polynomial dependence.Increased air temperatures at a number of production sites, air pollution with dust and gases require a revision of the list of personal protective equipment, depending on the class of working conditions, taking into account the established standards for output.The authors provide recommendations for the introduction of a new range of personal protective equipment, depending on the presence and degree of exposure to harmful factors of production.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".