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
Many fields, ranging from bioinformatics to databases to large-scale integrated circuits, deal with the interrelation between elementary objects. Objects are represented as vertices and the relationships between them are represented as nets (edges that connect two or more vertices) in the form of a hypergraph. We seek a placement of vertices that groups like objects and separates unlike objects. This involves separating related objects into a few, possibly disjoint, blocks. These hypergraph-partitioning problems are NP-hard so cannot be solved exactly, except for very small instances. We develop and use a numerical technique based on eigenvector decomposition of the connectivity matrix associated with the circuit netlist to partition hypergraphs emanating from circuit netlists. The eigenvector components of the circuit connectivity matrix are then used to determine vertex coordinates in one dimension that are then rounded in some fashion to determine block assignments. The inherent difficulty with eigenvector techniques is that the eigenvector components tend to cluster, making it difficult to determine correct block assignments. Our technique uses weights on nets, vertices, and fixed vertices to obtain a more “discrete” placement of vertices, making it easier to determine correct block assignments.
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