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
Abstract The Block Tree is a data structure for representing repetitive sequences in compressed space, which reaches space comparable with that of Lempel–Ziv compression while retaining fast direct access to any position in the sequence. In this paper, we generalize Block Trees to two dimensions, in order to exploit repetitive patterns in the representation of images, matrices and other kinds of bidimensional data. We demonstrate the practicality of the two-dimensional Block Trees (2D-BTs) in representing the adjacency matrices of Web graphs, and raster images in GIS applications. For this purpose, we integrate our 2D-BT with the $k^2$-tree—an efficient structure that exploits clustering and sparseness to compress adjacency matrices—so that it also exploits repetitive patterns. Our experiments show that this structure uses 60–80% of the space of the original $k^2$-tree, while being 30% faster to three times slower when accessing cells.
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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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