Bibliographic record
Abstract
The binary k-out-of-n system is a commonly used reliability model in engineering practice. Many authors have extended the concept of binary k-out-of-n system to multi-state k-out-of-n systems, but with a limitation that k is assumed to be a constant at all the system levels. In this paper, a new definition of the multi-state k-out-of-n system is presented. Under the proposed definition, maintaining at least a certain system state level may require a different number of components to be at a certain state or above. The multi-state k-out-of-n system model has more complex properties than binary k-out-of-n systems. Increasing and decreasing multi-state k-out-of-n systems are two special types of the multi-state k-out-of-n system. The increasing multi-state k-out-of-n system has the dominant property, and as a result, we can treat it as a binary k-out-of-n system for each fixed required system state level. The decreasing multi-state k-out-of-n system does not belong to the dominant multi-state system group, and consequently, we can not extend all results from the binary k-out-of-n system to it. Examples are given to illustrate that the multi-state k-out-of-n system model can be used to describe various engineering systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".