Bibliographic record
Abstract
Abstract-We consider the I/O-efficient rectangular segment search problem in 2D. The problem involves storing a given set S of N line segments in a data structure such that an axis aligned rectangular range query R can be performed efficiently; i.e., report all line segments in S which intersect R. We give a data structure requiring space O(N(N/B)2) disk blocks that can answer a range query R using O(IogBN + K/B) I/Os, where B is the number of line segments transferred in one I/O, and K is the number of line segments intersecting R. Search complexity of O(logB(N/B) + K/B) I/Os can be achieved with reduced storage if the set S contains only non-intersecting line segments, or if set S contains only horizontal and vertical line segments. In the former case the space complexity is O((N/B)2) disk blocks and in the latter case the space complexity is O(N log N/log logBN). We also consider the problem of finding all the line segments which are entirely within the rectangle R if the set S contains only vertical and horizontal line segments. For this problem, an optimal data structure is presented with size O(N log N/log logBN) disk blocks that requires O(logB(N/B) + K/B) I/Os to answer the query.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".