On the separation and equivalence of paging strategies
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Bibliographic record
Abstract
It has been experimentally observed that LRU and variants thereof are the \npreferred strategies for on-line paging. However, under most proposed \nperformance measures for on-line algorithms the performance of LRU is the same \nas that of many other strategies which are inferior in practice. In this paper \nwe first show that any performance measure which does not include a partition \nor implied distribution of the input sequences of a given length is unlikely to \ndistinguish between any two lazy paging algorithms as their performance is \nidentical in a very strong sense. This provides a theoretical justification for \nthe use of a more refined measure. Building upon the ideas of concave analysis \nby Albers et al. [AFG05], we prove strict separation between LRU and all other \npaging strategies. That is, we show that LRU is the unique optimum strategy for \npaging under a deterministic model. This provides full theoretical backing to \nthe empirical observation that LRU is preferable in practice.
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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