A data-driven model for safety risk identification from flight data analysis
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
Most aviation accidents take place in the final phase of a flight. One possible accident is the runway overrun - the fact that an aircraft leaves the runway unexpectedly on landing. Even though such accidents are well documented and studied in the aviation industry, this paper aims at identifying less direct links between data recorded by planes and the risk of runway overrun, or linked events. Indeed, a better understanding of these events using available flight data helps to reduce their number. Nonetheless, such analysis is not straightforward given the massive volume of data collected during the flights. For that purpose, we propose a data-driven approach with the use of data analysis methods and machine learning tools. After a quick correlation analysis, a boosted tree classifier was trained to classify flights as safe or at risk. The classifications were accurate enough to extract contributing factors, and a more extensive analysis was conducted on multiple airports. That analysis revealed the importance of particular factors, leading to new insights about potential approaches to aviation safety.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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