Bibliographic record
Abstract
Rapid identification of the cause of failure is a high priority in the immediate aftermath\nof a major civil aircraft accident. Attention is often focused on the two recorders, the\nCockpit Voice Recorder (CVR) and flight data recorder. In the event of sudden,\ncatastrophic loss of an aircraft through explosions or structural failure decompressions,\nthe recordings are seen as even more important. Yet these recorders are not designed to\nrecord such events with great fidelity and the ability of accident investigators to\ninterpret such recordings has been severely tested in several major accidents in the past\nthirty years; comparisons between accident recordings have not been able to produce\nconclusive results. This paper reports on a programme investigating CVR recordings of\nexplosions and rapid decompressions on a variety of aircraft from trials in several\ncountries. In particular we show that CVR recordings are generally unable to\ndiscriminate between explosions and structural failure decompressions and we explain\nwhy this is so. We shall also put forward practical suggestions for systems that may be\nable to record these events with greater fidelity and which would provide investigators\nin the future with tools to locate the seat of the failure.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".