Civil Aviation Occurrences in Indonesia
Bibliographic record
Abstract
Globally, civil air traffic has been growing rapidly in recent years, and with this growth, there has been a considerable improvement in air safety. However, in Indonesia, the recent rate of incidents and accidents in aviation is far higher than the global average. This study aims to assess civil aviation safety occurrences in Indonesia and, for the first time, to investigate factors contributing to these occurrences within commercial Indonesian aviation operations. In this study, 97 incident/accident investigation reports published by the Indonesian National Transportation Safety Committee between 2007 and 2015 were analysed. The most common occurrences involved Runway Excursions, Loss of Control In-Flight, and Controlled Flight into Terrain. In terms of the likelihoods of the occurrences and the severity of consequences, Runway Excursions were more common while Loss of Control In-Flight and Controlled Flight into Terrain events were more severe and often involved fatalities. In Indonesia, Runway Excursions were usually nonfatal and comprised 45% of the occurrences for commercial flights, compared to 34% globally. Further, in this study, weather and Crew Resource Management issues were found to be common contributing factors to the occurrences. Weather was a contributing factor for almost 50% of the occurrences involving Indonesian commercial flights. Adverse weather contributed to Loss of Visual Reference for visual flight operations in mountainous areas, which contributed to the majority of Indonesian fatal accidents. The combination of Indonesian monsoon climate and mountainous weather characteristics appears to provide many risks, mitigation of which may require specialist pilot training, particularly for multicrew aircraft. In identifying the main contributing factors, this study will hopefully provide motivation for changes in training and operations to enhance future aviation safety in Indonesia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".