Model-based Rapid Redesign Using Decomposition Patterns
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a pattern-based decomposition methodology for rapid redesign to support design customization in agile manufacturing of evolutionary products. The methodology has three functional phases. The first phase, called design dependency analysis, systematizes and reorganizes the intrinsic coupling structure of a given existing design model that is represented using the design dependency matrix. The second phase, called redesign partitioning analysis, generates alternative redesign pattern solutions to form a solution selection space through a three-stage procedure. The third phase, called pattern selection analysis, finds an optimal redesign pattern solution that entails the least potential redesign effort (in the subsequent solution process). Each pattern solution identifies and delimits the portions of the design model that need to be recomputed, thus expediting the redesign solution process. In such a way, one can treat the recomputation of the entire model, which is a conventional and computation-expensive solution approach, only as the last resort to solve the redesign problem given. An example redesign problem is used for the methodology illustration.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it