Why this work is in the frame
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Bibliographic record
Abstract
Let G = (V, E) be an edge-weighted complete graph representing a network in which the edges represent potential links, and the vertices (centres) are partitioned into two classes – vital vertices, which represent the vital core of the network, and secondary vertices. We consider the vital core connectivity problem (VCC), which is the problem of finding a minimum weight spanning multi-subgraph of G which is k-edge connected overall and whose vital core remains at least l-edge connected even if some or all of the secondary vertices are removed. The VCC arises naturally in many practical applications in which one wishes to design a network at minimum cost which will not only survive the loss of a certain number of links overall, but for which the vital core remains at least l-edge connected even if some or all of the secondary centres are lost. We show that the VCC is, in general, NP-hard, and present the first constant factor approximation algorithm for this problem, as well as give an upper bound on the integrality gap of its linear programming relaxation. In particular, we show an approximation guarantee (and upper bound on the integrality gap) of 8 3 for l ≥ ⌈
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 | 0.001 | 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