Bibliographic record
Abstract
The PR-tree data structure is introduced to characterize the sets of path-tree models of path graphs. We further characterize the sets of directed path-tree models of directed path graphs with a slightly restricted form of the PR-tree called the Strong PR-tree. Additionally, via PR-trees and Strong PR-trees, we characterize path graphs and directed path graphs by their Split Decompositions. Two distinct approaches (Split Decomposition and Reduction ) are presented to construct a PR-tree that captures the path-tree models of a given graph G = (V, E) with n = |V| and m = |E|. An implementation of the split decomposition approach is presented which runs in O (nm) time. Similarly, an implementation of the reduction approach is presented which runs in O (A(n + m) nm) time (where A(s) is the inverse of Ackermann's function arising from Union-Find [40]). Also, from a PR-tree, an algorithm to construct a corresponding Strong PR-tree is given which runs in O (n + m) time. The sizes of the PR-trees and Strong PR-trees produced by these approaches are O (n + m) with respect to the given graph. Furthermore, we demonstrate that an implicit form of the PR-tree and Strong PR-tree can be represented in O (n) space.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".