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Record W2317038184 · doi:10.2514/6.2014-3001

'Projet Epervier': Design, Build and Flight Test of a Full-Scale Aerobatic Aircraft at the Universite de Sherbrooke

2014· article· en· W2317038184 on OpenAlexaffabout
David Rancourt, Mathieu Lavoie, François Charron

Bibliographic record

Venue14th AIAA Aviation Technology, Integration, and Operations Conference · 2014
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAeronauticsScale (ratio)Flight testTest (biology)Aerospace engineeringComputer scienceEngineeringGeologyGeographyCartography

Abstract

fetched live from OpenAlex

The Universite de Sherbrooke in Canada requires the completion of a large scale design project by the end of the undergraduate degree in mechanical engineering. However, the scope and the scale of the projects at Sherbrooke differ significantly compared to those achieved in most US aerospace schools. Over a period of about two years, teams must go through the complete design process from the project definition to the manufacturing and testing of the system. Not only they must complete the project on time, the students also have the responsibility to gather the funding and develop their own sponsorship program. This paper describes the main characteristics of this unique program that allows large scale projects to be completed with success, such as the “Projet Epervier”. Between 2006 and 2008, a group of 12 undergraduate students in mechanical engineering invested over 12,000 hours to design, manufacture, and flight test a 750 lbs, one-seater, fully aerobatic aircraft.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2014
Admission routes2
Has abstractyes

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