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Record W3126680905 · doi:10.15394/ijaaa.2021.1518

Folding Methodology for Flexible Aircraft Interiors

2021· article· en· W3126680905 on OpenAlexaff
Aditya Venkatesh, Fengfeng Xi, J.Y. Chung

Bibliographic record

VenueInternational Journal of Aviation Aeronautics and Aerospace · 2021
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFolding (DSP implementation)Control reconfigurationProcess (computing)Computer scienceSpace (punctuation)Architectural engineeringSystems engineeringEngineeringMechanical engineeringEmbedded system

Abstract

fetched live from OpenAlex

This paper establishes a general furniture folding methodology that is aimed at flexible aircraft cabin interiors. This methodology will allow users to modify existing furniture pieces to their liking and thereby customizing their overall travel experience. The folding methodology also includes a process to quantify the transformation of the furniture pieces. This paper also introduces two design concepts called Open-on-Demand and Reconfiguration to allow passengers to modify an otherwise rigid cabin. The advantages of the multi-functional space saving furniture pieces are also illustrated in this paper. While the general folding methodology was developed for aircraft interiors, it can be adapted for any furniture piece positioned within any environment.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score0.341

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 head0.029
GPT teacher head0.289
Teacher spread0.260 · 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
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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