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Record W2044186125 · doi:10.1117/12.389163

<title>Effects of field-of-view on pilot performance in night vision goggles flight trials: preliminary findings</title>

2000· article· en· W2044186125 on OpenAlexaff
Sion Jennings, Greg Craig

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsDegree (music)Task (project management)Orientation (vector space)Computer scienceVisual fieldAltitude (triangle)AeronauticsLongitudinal fieldSimulationComputer visionArtificial intelligencePsychologyMathematicsEngineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

Night vision goggles (NVGs) allow pilots to see and navigate under minimal levels of illumination. While NVGs allow the user to see more than they typically could under these levels of illumination, the visual information provided by NVGs has a limited field-of-view. The size of the field-of- view can diminish the pilot's spatial orientation ability in the night flying environment. We examined pilot performance in low level helicopter flight while the pilots were using NVGs with 40 degree(s), and 52 degree(s) fields-of-view. The pilots flew a standardized ADS-33D hover maneuver in a Bell 206 helicopter equipped with an accurate position measurement system. The tests were conducted in simulated night conditions and both subjective and objective measures of task performance were obtained. Pilot Cooper-Harper ratings increased from Level 1 baseline ratings to Level 2 ratings when the NVGs were used, indicating worse performance when using the NVGs. Small rating differences were noticed between the 52 degree(s) and 40 degree(s) field-of-view conditions. Similar trends were noticed in the objective data of altitude, and lateral and longitudinal station keeping errors.

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 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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations9
Published2000
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicImpact of Light on Environment and HealthFrench-language works237,207