The Need for Establishing a Commercial Aviation Safety Council to Prevent Aircraft Accidents
Bibliographic record
Abstract
The government has significantly strengthened safety measures since the 2013 San Francisco plane crash, and there have been no fatalities until 2023. However, the worst domestic aircraft accident occurred in December 2024, requiring continuous and innovative measures. In order to prevent additional fatal aviation accidents, safety management must be operated before an accident occurs, not after, through cooperation with airlines and data-based preventive safety management. The United States established the CAST(Commercial Aviation Safety Team) in 1997 after the 1996 TWA(Trans World Airlines) accident, which led to a significant decline in aviation safety. Canada also established C-CAST and has played a global role in flight data analysis since the mid 1980s by working with airlines. Looking at the innovation cases of other states, establishing a permanent commercial aviation safety council that communicates with airlines and experts and utilizes data to manage aviation safety before an accident occurs is a golden opportunity for innovation.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".