TWO-LEVEL BURN-IN FOR RELIABILITY AND ECONOMY IN REPAIRABLE SERIES SYSTEMS HAVING INCOMPATIBILITY
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
When a system is assembled from components, incompatibility often occurs as a result of the assembly process. The ability to quantify incompatibility is very important for making burn-in decisions because the goal of system burn-in is to minimize the incompatibility factor. In the past, incompatibility has been only partially represented in the system prediction models because it was assumed that assembly had no effect on the components. This paper presents a more accurate model for system prediction by allowing for the possibility that, in some cases, assembly adversely affects the components. After applying a superposition of delayed renewal processes and a nonhomogeneous Poisson process for modeling times between system failures, we derive and analyze the effects of component and system burn-in on the system cost and performance. Examples are included to demonstrate how to determine optimal component and system burn-in times simultaneously based on an equivalent problem formation and nonlinear programming.
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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 | 0.003 | 0.001 |
| 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 | 0.001 |
| 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