Human factors in helicopter accidents: results from the analysis performed by the European Helicopter Safety Analysis
Bibliographic record
Abstract
The European Helicopter Safety Team (EHEST) is the European branch of the International Helicopter Safety Team (IHST) and the rotorcraft component of the European Strategic Safety Initiative (ESSI). In 2008, the European Helicopter Safety Analysis Team (EHSAT) of the EHEST has analysed 186 helicopter accidents reported by the Accident Investigation Boards (AIBs) within timeframe 2000-2005 and State of occurrence located in Europe. EHSAT analyses are based on a standard method adapted by the Joint Helicopter Safety Analysis Team (JSAT), the analysis team of IHST, from the US Commercial Aviation Safety Team (CAST). The European team has included HFACS to enrich the analysis of human factors involved in the accidents. The paper presents this European helicopter safety initiative and focuses on human performance related analysis results. It concludes by presenting the benefits of using HFACS in addition to the Standard Problem Statements (SPS) analysis taxonomy by the JSAT. EHEST: The European component of the International Helicopter Safety Team (IHST) IHST was established after the first International Helicopter Safety Symposium (IHSS) held in Montreal in September 2005. IHST is a combined government and industry effort to reduce the helicopter accident rates (both civil accidents and noncombat military mishaps) by 80% within 10 years in the US and worldwide. See
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".