Model-Based Decomposition Using Non-Binary Dependency Analysis and Heuristic Partitioning Analysis
Bibliographic record
Abstract
The two-phase method for model-based decomposition (Chen et al. 2005a) has two major functional components: dependency analysis and partitioning analysis. The functions of these two components are enhanced and generalized in this paper in order to improve the method’s capability. On the one hand, the non-binary dependency analysis is developed such that the two-phase method can handle both binary and non-binary dependency information of the model. The essence of this development is to properly select a resemblance coefficient for the quantification of couplings among the model’s elements. On the other hand, as the past version of partitioning analysis takes the enumerative approach to search decomposition solutions, the heuristic partitioning analysis is developed as an alterative to search a reasonably good solution in a shorter time. The working principle of the heuristic approach is to analyze the coupling structure of the model such that the weak coupling links among the model’s elements can be identified for model partitioning. At the end, a relief valve system is applied to illustrate and justify the newly developed method components.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".