A Weighted Freshness Metric for Maintaining Search Engine Local Repository
Bibliographic record
Abstract
Current search engines maintain a local repository to improve the search efficiency. A crawler is used to periodically poll the remote web pages to update the contents of the local repository. Due to the resource limitations, some local pages may be stale. To maintain the high freshness of the repository, the crawler is expected to revisit remote web pages in optimized order and frequency. The intuitive metric of freshness of the local repository is defined as the fraction of up-to-date web pages in the repository, which is merely based on the repository content, and does not, unfortunately, reflect the perspective of the search engine users, e.g., how often is a web page queried? We propose a novel weighted metric of the repository freshness with the importance of web pages being the weights. This metric not only takes into account the local web pages themselves but also the perspectives of the search engine users. We study the repository synchronization policy under this new metric, compare this metric with others, analyze its features, and discuss how the web page importance is determined.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".