Graph Encoding and Recursion Computation
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
It is a general opinion that relational database systems are inadequate for manipulating composite objects that arise in novel applications such as 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. Especially, when recursive relationships are involved, it is cumbersome to handle them in relational databases, which sets current relational systems far behind the navigational ones (Kuno & Rundensteiner, 1998; Lee & Lee, 1998). To overcome this problem, a lot of interesting graph encoding methods have been developed to mitigate the difficulty to some extent. In this article, we give a brief description of some important methods, including analysis and comparison of their space and time complexities.
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 | 0.000 |
| Open science | 0.000 | 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