Optimal Replacement Last With Continuous and Discrete Policies
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes age and periodic replacement last models with continuous, and discrete policies. That is, an operating unit is replaced preventively at time T of operation as a strategic policy, or at a number N of working cycles to satisfy successive job completion, whichever occurs last. Such policies are named as replacement last, and their expected cost rates and optimal policies are obtained. However, the focus of this paper is to compare replacement last with replacement first policies, which are formulated under the classical assumption of whichever occurs first. From the points of cost and performability, different comparative methods for continuous and discrete optimizations are demonstrated to determine in what cases we should adopt replacement last rather than replacement first. All theoretical discussions in this paper are made analytically, and are computed numerically.
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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 | 0.000 | 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