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Record W2320277382 · doi:10.2514/6.2005-2127

The Application of the MISTC Framework to Structural Design Optimization

2005· article· en· W2320277382 on OpenAlexaff
Keith Hunten, Maxwell Blair

Bibliographic record

Venue46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsLockheed Martin (Canada)
FundersAir Force Research LaboratoryNational Aeronautics and Space AdministrationU.S. Department of Defense
KeywordsComputer scienceSystems engineeringEngineering

Abstract

fetched live from OpenAlex

The SBAAT program enables technology integration studies in the development of structural concepts that have no historical precedence, including process-based affordability assessments. The MISTC design framework enables the capability to perform extensive high fidelity technology assessment studies at the system level, and has a planned and partially prototyped link to high-level scenario based simulation. The basic concept for MISTC is a parametric design/analysis development, assessment, and optimization system whose linked model representations enable quick alternative studies. After three years under development, application studies have proven that MISTC reduces design cycle time for mechanical-structures concept definition, integrity analysis, and manufacturing cost assessment by a conservatively estimated 75%.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.030
GPT teacher head0.301
Teacher spread0.271 · 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 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

Citations10
Published2005
Admission routes1
Has abstractyes

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