CREATION OF A "SMART" OCCUPATIONAL SAFETY MANAGEMENT SYSTEM IN CIVIL AVIATION IN THE CONTEXT OF THE "SOCIETY 5.0" CONCEPT
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it