Bibliographic record
Abstract
Composite objects represented as directed graphs are an important data structure that require efficient support Web and document databases (Abiteboul, Cluet, Christophides, Milo, Moerkotte, & Simon, 1997; Chen & Aberer, 1998, 1999; Mendelzon, Mihaila, & Milo, 1997; Zhang, Naughton, Dewitt, Luo, & Lohman, 2001), CAD/ CAM, CASE, office systems, and software management. It is cumbersome to handle such objects in relational database systems when they involve ancestor-descendant relations (or say, reachability relations). In this article, we present a new graph encoding based on a tree labeling method and the concept of branchings that are used in the graph theory for finding the shortest connection networks. A branching is a subgraph of a given digraph that is in fact a forest, but covers all the nodes of the graph. Concretely, for a DAG G (directed acyclic graph) of n nodes, the space needed for storing its transitive closure can be reduced to O(b·n), where b is the number of the leaf nodes of G’s branching. Such a compression is, however, at the expense of querying time. Theoretically, it takes O(logb) time to check whether a node is reachable from another. The method can also be extended to digraphs containing cycles.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".