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Record W2025567884 · doi:10.2514/6.2006-7786

Facilitating New Technology Introduction through Monitoring and Controlling System Constraints

2006· article· en· W2025567884 on OpenAlexaff
Juliana Early, Mark Price, Richard Curran, Srinivasan Raghunathan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceSystems engineeringEngineering

Abstract

fetched live from OpenAlex

High value systems are required to be able to adapt and cope with change, as the requirements for flexibility in system design mean that not only do systems need to be capable of adapting to evolving design environments, but also evolving design requirements. Current complex engineering systems are being designed for increasingly longer design lifetimes, driven by (and often justified by) economic analysis. While this has short term benefits, it overlooks the issues surrounding obsolescence before the anticipated retirement date due to rapid technology advancement. In many cases, the initial circumstances from which the original system requirements were derived have changed or been modified, and as such flexibility is a key property which should be embedded in so-called ‘high-value’ assets. When considering modifications to system components to respond to these changing needs, it is critical to have a clear and complete understanding of the implications of such modifications not only on the local system performance, but on all the systems which reference it through data exchange. The current work proposes a methodology by which the design space may be investigated in order to determine instances in which no viable design solution exists, and facilitates a method by which constraints may be relaxed and/or shifted in order to expand the allowable design space. This is of specific interest to cases in which new technology is introduced into an existing system, and this will be specifically addressed with regards as to how ascertain the feasibility of incorporating advanced technology into existing system architectures. The paper presents the manner in which the implications of aircraft system upgrading can be assessed, and how the identification of those linkages which are impacted by the introduction of a new system may be used in order to ensure that new systems will operate seamlessly with existing system components, or facilitate a shift in the design space to enable new operating regimes.

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)0.006
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0070.008
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.006
GPT teacher head0.207
Teacher spread0.202 · 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 designTheoretical or conceptual
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

Citations0
Published2006
Admission routes1
Has abstractyes

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