Quantitative Safety Risk Assessment using Aviation Safety Data
Bibliographic record
Abstract
To manage State Safety Program (SSP) in a more integrative and proactive manner, an aviation safety authority of the state shall detect and assess the risk of emerging or hidden safety hazards before they provoke accidents or incidents( ICAO, 2018 ). In case of South Korea, safety risk assessment is conducted by calculating the likelihood and severity of the hazard following ICAO’s safety management manual. It is reasonable to extract the safety risk likelihood by calculating the number of occurrence caused by the hazard. However, it is ambiguous to assess the safety risk severity defined as the extent of harm that might be expected to occur as a consequence of the identified hazard. In this paper, a safety risk assessment method which quantitatively calculates the risk of hazard using aviation safety data(i.e. aviation safety mandatory report, etc.) is proposed. By utilizing the proposed method, the existing process that safety risk is being subjectively assessed by safety inspectors can be supplemented. So that essential aviation safety policy decision making can be accomplished by the accurate result of safety risk assessment.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".