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Record W4316653891 · doi:10.12985/ksaa.2022.30.3.117

The Effective Use of Basic Aviation Training Device (BATD) and the Analysis of Flight Training Effectiveness

2022· article· en· W4316653891 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Korean Society for Aviation and Aeronautics · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsInternational Civil Aviation Organization
Fundersnot available
KeywordsCockpitFlight trainingFlight simulatorTraining (meteorology)Flight management systemAeronauticsAviationComputer scienceSimulationEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

A method to increase the effectiveness of flight training at a low cost by applying the correct flight training method to students without flight experience is a very important factor. BATD equipped with an extended display device enables the proper cross-check of external references and internal instruments by integrated flight instruction methods, enabling effective flight training in the initial stages. In addition, BATD employs the Cessna 172 model with Glass Cockpit to help make it easy to apply to actual flights. As a result of analyzing the effect of flight training through a survey of students who completed the BATD practice lecture, it was very helpful to understand the theories related to flight that they had already learned, and they responded that they could easily adapt to all flight subjects in additional FTD practice lectures. Therefore, a well-planned BATD practice lecture will be easy to adapt to real flight training, which will have significant effects in reducing time and cost.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.242
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it