On the separation and equivalence of paging strategies
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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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 | 0.006 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".