Extended Hierarchical Cluster Analysis for the Decomposition of Complex Design Problems
Bibliographic record
Abstract
This paper presents a new decomposition method for partitioning complex design problems based on an extended Hierarchical Cluster Analysis (HCA). After a complex design problem is represented using a function-parameter incidence matrix, this new decomposition method allows transforming the originally unorganized matrix into a block-angular form matrix. By means of the resulting matrix, a coordination part and design blocks can be further identified and obtained. In particular, the extended HCA plays an important role in this method, contributive to aligning all non-zero elements, also known as 1s elements, of the matrix along its main diagonal as compactly as possible. As such, a post process, called Partition Point Analysis (PPA), can be further applied to the matrix to finally form the coordination part and the related design blocks, subject to such decomposition criteria as block size and coordination size limits. A powertrain design example is employed for illustration of the decomposition method newly developed.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".