MétaCan
Menu
Back to cohort
Record W4317665979 · doi:10.2514/6.2023-1772

An Assessment of the Environmental and Cost Benefits from the Electrification of Light Trainer Aircraft

2023· article· en· W4317665979 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 SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsElectrificationAirframeAutomotive engineeringAviationAeronauticsGreenhouse gasEnvironmental impact assessmentCertificationAutomotive industryEnvironmental scienceEngineeringComputer scienceAerospace engineeringElectricityElectrical engineering

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-1772.vid In order to reduce the environmental impact of aviation, many are looking at the electrification of aircraft as a potential solution to curb direct and indirect combustion greenhouse gas emissions in the atmosphere. While battery-electric aircraft show limited near-future applicability for large commercial airliners, ab-initio flight schools are already adopting this technology. This paper presents a fair assessment of the environmental and cost benefits of a retrofit all-electric light trainer aircraft by comparing it not only to its original airframe, but also to a smaller conventional aircraft capable of accomplishing the same mission, as well as to a version of this aircraft equipped with automotive emission control technology.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.248

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 head0.013
GPT teacher head0.275
Teacher spread0.263 · 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