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Record W2333086033 · doi:10.2514/6.2000-4073

G-cueing for fighter simulation training

2000· article· en· W2333086033 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

VenueModeling and Simulation Technologies Conference · 2000
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsWorkloadComputer scienceSet (abstract data type)Motion (physics)Knowledge basePath (computing)Base (topology)Control (management)Training (meteorology)SimulationHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

An operator can be regarded as a set of sensors with a feedback algorithm (reflexes and skills), rules, and a knowledge base that are used in stabilizing and controlling an aircraft in a given flight path. Motion cueing devices play a role in the training tool by enabling the trainee to develop proper control strategy (tuning the gains of the feedback algorithm), and to acquire parts of the required set of rules and knowledge base (by simulating representative workload, for example). The motion system and the g-seat both have relative advantages and disadvantages and the choice of one, the other, or both must be made on the basis of an optimal alignment between the training objectives and the capabilities of the training device.This paper will describe an optimized g-cueing seat design and compare its training potential with a standard Stewart motion platform by evaluating their respective effects on the pilot control loop, on the required workload and on their effectiveness in facilitating the training of rulebased and knowledge-based behaviors.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.584
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.050
GPT teacher head0.263
Teacher spread0.214 · 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