The influence of implementing electronic flight bag application on aviation safety mediated by the optimization of human resources
Bibliographic record
Abstract
The unintegrated use of information technology in the cockpit of Garuda aircraft was the electronic flight bag, with the application of the Garuda electronic flight manual and the Garuda electronic airway manual. This gap would cause potential negligence and delay in distributing paper documents or manuals to the aircraft of Garuda Indonesia Airline. It was necessary to study whether the use of Garuda electronic flight manual software and Garuda electronic airway manual could ease the duties of the Pilot on board the aircraft. This research aimed to know the influence of implementing the Garuda electronic flight manual and the Garuda electronic airway manual on flight safety by optimizing human resources. The study used the Path Analysis method. The samples of this research were 30 pilots as the users and processors of the Garuda electronic flight manual and the Garuda electronic airway manual. The study found that the variable of flight safety was directly influenced by the implementation of the Garuda electronic flight manual, the Garuda electronic airway manual, and the optimization of human resources. In addition, implementing the Garuda electronic flight manual and the Garuda electronic airway manual was the variable influencing flight safety at most.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".