CREATION OF A "SMART" OCCUPATIONAL SAFETY MANAGEMENT SYSTEM IN CIVIL AVIATION IN THE CONTEXT OF THE "SOCIETY 5.0" CONCEPT
Bibliographic record
Abstract
This article examines current challenges in occupational safety and health in civil aviation in the Republic of Kazakhstan, including the growth of traffic volumes, increasing number of flights, and increasing complexity of technological processes. This demonstrates that traditional approaches that focus on monitoring violations and mitigating the consequences of incidents are limited in effectiveness and do not systematically prevent industrial risks. This article substantiates the need to transition to a "smart" occupational safety and health management system based on digitalization and intellectualization, the use of predictive analytics, automated monitoring tools, and the creation of a unified database for risk factor analysis. The proposed concept enables a shift from reactive to proactive occupational safety management, which reduces injury rates, improves the reliability of enterprise operations, and optimizes labor-resource utilization. It is emphasized that the implementation of a "smart" occupational safety and health system that complies with international standards will enhance the sustainability and competitiveness of Kazakhstan's aviation industry and will ensure its adaptation to global trends in technological development and increasing workload.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".