Tree-Based Dependency Analysis in Decomposition and Re-decomposition of Complex Design Problems
Why this work is in the frame
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Bibliographic record
Abstract
We have developed a formal method for decomposition of complex design problems in two phases: dependency analysis and matrix partitioning. Of the most distinct characteristic in this method is the support of cost-effective re-decomposition (as is often required in decomposition solution synthesis), where dependency analysis serves as a platform for the enabling of re-decomposition. Yet, this requires that the result of the dependency analysis be robust and thus reusable for re-decomposition. In this paper, after revealing the deficiency in the current practice of dependency analysis, we present an enhanced dependency analysis method that is built on ordinary tree structure (instead of binary tree structure). This new approach, which is more systematic, ensures robust dependency analysis, whose result is insensitive to the arrangement of a tree structure in tree-based dependency analysis. A complete set of tree-based algorithms is also provided, along with their applications to two design examples
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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 | 0.000 |
| 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