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Record W4382047996 · doi:10.32920/23581176

Design Of Flight Deck Displays For General Aviation Electric Aircraft

2023· preprint· en· W4382047996 on OpenAlexaff
Maryam Safi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCockpitSituation awarenessAviationAeronauticsWorkloadTouchscreenEngineeringAviation safetyAutomotive engineeringDeckHazardSimulationComputer scienceAerospace engineeringHuman–computer interaction

Abstract

fetched live from OpenAlex

Human factors can ensure air safety through the creation of user centred designs. Flight deck displays for an electric general aviation aircraft were designed based on human factors considerations. A list of powerplant indicating instruments for electric aircraft were established based on available information for fuel powered aircraft and electric automotive vehicles. The design uses two touchscreen displays to present primary flight and navigation instruments, powerplant indicating instruments, a warning display, and an electronic Pilot Operating Handbook (POH). The electronic POH retrieves and displays recovery procedure for identified hazard states. The flight deck displays were evaluated for visual efficiency using the Konect Value method. Results show that the design meets visual efficiency requirements, and has potential to increase pilot situational awareness, decrease cognitive workload, and support pilot performance in the flight.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.088
GPT teacher head0.396
Teacher spread0.308 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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