An Expert Approach to Assessing Technogenic Risk at Enrichment Plants
Bibliographic record
Abstract
This article presents a methodological approach to the assessment of technogenic risk based on expert assessments in the processing plants of Central Kazakhstan.During the expert study, the criteria were determined by which the composition of the expert group was formed, and special linguistic scales were developed to carry out the procedure for assessing risk indicators by experts.Expert studies were conducted on 10 possible types of accidents at enrichment plants in Central Kazakhstan.The assessment of the consistency of expert opinions was carried out using the Kendall concordance coefficient.As a new approach for assessing technogenic risk, technical and ecological criteria were identified and their influence on the probability and consequences of accidents was assessed.These parameters formed the basis for multifactorial mathematical models of hazard indicators and the severity of the consequences of an accident during ore processing and enrichment.Scales have been developed to assess the hazard and severity of accidents, a risk assessment matrix, as well as a description of accident risk levels.Corrective measures for enrichment plants have been proposed.This technique is applicable to various production processes.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".