Bibliographic record
Abstract
This paper examines the space-time performance of in-memory conjunctive list intersection algorithms, as used in search engines, where integers represent document identifiers. We demonstrate that the combination of bitvectors, large skips, delta compressed lists and URL ordering produces superior results to using skips or bitvectors alone. We define semi-bitvectors, a new partial bitvector data structure that stores the front of the list using a bitvector and the remainder using skips and delta compression. To make it particularly effective, we propose that documents be ordered so as to skew the postings lists to have dense regions at the front. This can be accomplished by grouping documents by their size in a descending manner and then reordering within each group using URL ordering. In each list, the division point between bitvector and delta compression can occur at any group boundary. We explore the performance of semi-bitvectors using the GOV2 dataset for various numbers of groups, resulting in significant space-time improvements over existing approaches. Semi-bitvectors do not directly support ranking. Indeed, bitvectors are not believed to be useful for ranking based search systems, because frequencies and offsets cannot be included in their structure. To refute this belief, we propose several approaches to improve the performance of ranking-based search systems using bitvectors, and leave their verification for future work. These proposals suggest that bitvectors, and more particularly semi-bitvectors, warrant closer examination by the research community.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".