An improved d-MP search algorithm for multi-state networks
Bibliographic record
Abstract
A Minimal Path (MP) vector for a system state d is called a d-MP. Most reported works on generating d-MPs are for a particular d value. If all d-MPs for all possible integer d values are required, we need to call those methods multiple times with respect to all d values. Virtually, each d-MP candidate can be generated by a combination of one (d-1)-MP and one binary minimal path vector. Thus, we can use binary MP vectors as building blocks to generate 2-MP candidates, and use 2-MPs and binary MPs as building blocks to generate 3-MP candidates ... and so forth. During the process, each newly generated candidate will be validated by certain constraints and real d-MPs are obtained. When the d-MPs with the maximum d value have been found, all the d-MPs for all possible integer d value are found as well. Based on the observations above, we report a recursive algorithm based on the concept of backtracking. By computational experiments, it is found that the proposed algorithm is more efficient than existing algorithms for finding all d-MPs for all possible integer d values. The generated d-MPs can be used for system state distribution evaluation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".