Towards Rapid Redesign: Decomposition Patterns for Large-Scale and Complex Redesign Problems
Bibliographic record
Abstract
In an effort to develop a decomposition-based rapid redesign methodology, this paper introduces the basis of such a methodology on decomposition patterns for a general redesign problem that is computation-intensive and simulation-complex. In particular, through pattern representation and quantification, this paper elaborates the role and utility of the decomposition patterns in decomposition-based rapid redesign. In pattern representation, it shows how a decomposition pattern can be used to capture and portray the intrinsic properties of a redesign problem. Thus, through pattern synthesis, the collection of proper decomposition patterns allows one to effectively represent in a concise form the complete body of redesign knowledge covering all redesign problem types. In pattern quantification, it shows how a decomposition pattern can be used to extract and convey the quantum information of a redesign problem using the pattern characteristics. Thus, through pattern analysis, the formulation of an index incorporating two redesign metrics allows one to efficiently predict in a simple manner the amount of potential redesign effort for a given redesign problem. This work represents a breakthrough in extending the decomposition-based solution approach to computational redesign problems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".