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Record W3035212369 · doi:10.2514/6.2020-2652

Lessons Learned from the Design, Manufacturing, and Test of an All-Electric General Aviation Aircraft

2020· article· en· W3035212369 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

VenueAIAA AVIATION 2020 FORUM · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAviationMultidisciplinary approachGeneral aviationEngineeringPoint (geometry)Systems engineeringTest (biology)AeronauticsComputer scienceAerospace engineering

Abstract

fetched live from OpenAlex

Following the success of the electrification of ground vehicles and the needs for clean aviation, research and development in electric aircraft have emerged. While all-electric and hybrid electric architectures might be key solutions, the lack of public shared experiences restrain the development of commercial solutions. The unshared knowledge prevents guiding simple, yet crucial design choices. This paper aims to share multiple lessons learned throughout the design, fabrication and ground testing of a manned, fully functional electric aircraft. Major aspects of the development will be asserted from a multidisciplinary point of view, both in mechanical and electrical engineering, in a field where public information is absent. A total of 12 lessons are presented.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.489

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 head0.045
GPT teacher head0.283
Teacher spread0.238 · 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