Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Consider a tree T on n nodes, each having a weight drawn from [1‥σ]. In this article, we study the problem of supporting various path queries over the tree T . The path counting query asks for the number of the nodes on a query path whose weights are in a query range, while the path reporting query requires to report these nodes. The path median query asks for the median weight on a path between two given nodes, and the path selection query returns the k -th smallest weight. We design succinct data structures to encode T using n nH ( W T ) + 2 n + o ( n lg σ) bits of space, such that we can support path counting queries in O (lg σ/lg lg n + 1)) time, path reporting queries in O (( occ +1)(lg σ / lg lg n + 1)) time, and path median and path selection queries in O (lg σ / lg lg σ) time, where H ( W T ) is the entropy of the multiset of the weights of the nodes in T and occ is the size of the output. Our results not only greatly improve the best known data structures [Chazelle 1987; Krizanc et al. 2005], but also match the lower bounds for path counting, median, and selection queries [Pătraşcu 2007, 2011; Jørgensen and Larsen 2011] when σ = Ω( n /polylog( n )).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it