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 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".