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
Cormen et al. describe efficient algorithms for inserting nodes into and deleting nodes from red-black trees. If some binary trees satisfying the definition of red-black trees cannot be built by these algorithms, then theoretical analyses of red-black trees that consider all binary trees satisfying the definition of red-black trees may not accurately describe the behavior of red-black trees in practice. We show that any binary tree shape that satisfies the definition of red-black trees can be built using only the insertion algorithm, RB-INSERT, of Cormen et al. We first describe an algorithm, RB-SHAPE, which, given any red-black tree T, will construct an insertion sequence for T. When the constructed sequence of insertions is performed on the empty tree using RB-INSERT, the result is a red-black tree with the same shape as T. We then prove the correctness of algorithm RB-SHAPE.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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