Bibliographic record
Abstract
We present dynamic search-tree data structures that perform well in the setting of a hierarchical memory (including various levels of cache, disk, etc.), but do not depend on the number of memory levels, the block sizes and number of blocks at each level, or the relative speeds of memory access. In particular between any pair of levels in the memory hierarchy, where transfers between the levels are done in blocks of size B, our data structures match the optimal search bound of /spl Theta/(log/sub B/ N) memory transfers. This bound is also achieved by the classic B-tree data structure, but only when the block size B is known, which in practice requires careful tuning on each machine platform. One of our data structures supports insertions and deletions in /spl Theta/(log/sub B/ N) amortized memory transfers, which matches the B-tree's worst-case bounds. We augment this structure to support scans optimally in /spl Theta/(N/B) memory transfers. In this second data structure insertions and deletions require /spl Theta/(log/sub B/ N+log/sup 2/N/B) amortized memory transfers. Thus, we match the performance of the B-tree for B=/spl Omega/(log N log log N).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".