Implementation of the digital training concept in the basic flight training in the Serbian Military Academy
Bibliographic record
Abstract
Introduction/purpose: The paper provides a review of recent research in the field of digital training applied in the Serbian Military Academy flight training. Flight training represents the foundation of successful education of military pilots. Its division is based on the environment and a phase of realization. The main part and the core of successful flight training is basic flight training. This training phase has experienced significant changes with the introduction of the Technically Advanced aircraft (TAA) which is characterized by a high degree of digitalization not only of the cockpit but also of other aircraft systems. Consequently, a different methodological approach to training is needed, including a digital training concept. The paper shows the achievements and certain solutions based on some elements of digital training concepts used in the basic flight training at the Serbian Military Academy. Methods: The scientific approach is used in the evaluation of aircraft cockpit digitalization and in the implementation of a new training concept in the basic flight training in the Serbian Military Academy. Results: Based on the methodological analysis used, the importance and the values of the digital training concept in basic flight training are shown. Conclusion: Although the digital training concept is not mandatory in the existing flight training model in the Serbian Military Academy, it proves to be a valuable asset. Its potential is significant and, to a certain extent, it can change the nature of basic flight training. Due to digital training, cadets can fly more safely and their flying skills are acquired faster. In accordance with new modern aircraft acquisition in the Serbian Air Force, every aspect of the digital training concept has to be carefully considered, especially in the basic flight training phase, including conversion to new aircraft types.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".