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Record W4383502120 · doi:10.4050/f-0079-2023-1386

Landing Phase Analysis of the NRC Bell 412 Advanced Systems Research Aircraft in Supervised Autonomous Flight Systems Configuration

2023· article· en· W4383502120 on OpenAlexaffabout
Marc Alexander, Greg Craig, Iryna Borshchova, Derek Gowanlock, Arthur Gubbels, Eris Ellis, Awantha Jayasiri, Tomas Naprstek, Bryan Carrothers, Sion Jennings, Perry Comeau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsModular designInterfacingObstacleAeronauticsLift (data mining)Computer scienceTakeoffFlight testSystems engineeringEngineeringAerospace engineeringSimulation

Abstract

fetched live from OpenAlex

The National Research Council of Canada (NRC) Flight Research Laboratory (FRL) is advancing research under its Canadian Vertical Lift Autonomy Demonstration (CVLAD) program. Modular architecture hosted on the NRC Bell 412 Advanced Systems Research Aircraft (ASRA) enables pilot supervised autonomous flight via customized multi-display guidance-cueing. The capability exploits ASRA's full-authority control and safety systems, inertial-radar navigation, state-based digital supervision and is supported by open-source mission planning and digital-twin simulation. To date, ground interfacing (takeoff, landing) trials have been executed in proximity to NRC facilities in Ottawa (Canada) with rich test site variability (surfaces, slopes, obstacles, seasonal variations). Applied knowledge has been generated for autonomous mission execution in broad weather, low-speed, low-altitude, and low obstacle-standoff conditions critical in the vertical lift flight autonomy research domain.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
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 head0.035
GPT teacher head0.312
Teacher spread0.277 · 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 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 routes2
Has abstractyes

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