Interoperable server-based cache consistency algorithm
Bibliographic record
Abstract
Numerous caching algorithms have been investigated for the client-server object database management systems. The algorithms not only ensure cache consistency by preventing applications' access to stale data, but they also support transactional properties. Caching algorithms have been classified in a number of ways - one classification is into avoidance and detection categories, depending on whether access to the stale data is avoided, usually by locking, or permitted and then any conflict detected at commit time. Detection-based algorithms have better performance but can lead to high abort rate that is unacceptable for interactive applications. It is for this reason that avoidance-based algorithms are usually adopted in practice. This work describes a server-based interoperable transactional caching algorithm that concurrently supports the leading avoidance-based (adaptive callback locking (ACBL)) and detection-based (adaptive optimistic concurrency control (AOCC)) algorithms. At a client either the avoidance or the detection caching algorithm is used without any changes. It is the server-side caching algorithm that concurrently supports both avoidance and detection client-side caching.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".