Multi-state k-out-of-n system model and its applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it