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Record W2026717679 · doi:10.2514/1.j052180

Development of a Multilevel Multidisciplinary-Optimization Capability for an Industrial Environment

2013· article· en· W2026717679 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 Journal · 2013
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Multi-Objective Optimization Algorithms
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsMultidisciplinary design optimizationMultidisciplinary approachSystems engineeringSoftware deploymentEngineering optimizationComputer scienceFrame (networking)Industrial engineeringEngineeringOptimization problemSoftware engineeringMechanical engineering

Abstract

fetched live from OpenAlex

An overview of the deployment of multidisciplinary-optimization technologies in an industrial environment is presented herein. This capability is being developed in a multilevel framework in line with the aircraft design stages within the engineering organization. At every design stage, the appropriate problem formulation, level of detail, analysis tools, and optimization strategy are implemented to meet the design objectives within the design-cycle time frame. The multidisciplinary-optimization technologies are deployed incrementally as an evolution of existing engineering methods, with subject-matter experts contributing to the problem setup and validation of the framework. A description of the multilevel strategy and sample results are provided.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.251
Threshold uncertainty score0.607

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 communication0.0000.001
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 head0.054
GPT teacher head0.284
Teacher spread0.231 · 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