Bibliographic record
Abstract
On the distribution of values in the quadratic assignment problem by A. Barvinok and T. Stephen Modeling and optimization in massive graphs by V. Boginski, S. Butenko, and P. M. Pardalos A tale on guillotine cut by M. Cardei, X. Cheng, X. Cheng, and D.-Z. Du Wavelength assignment algorithms in multifiber networks by M. X. Cheng, Z. Gong, X. Huang, H. Zhao, X. Jia, and D. Li Indivisibility and divisbility polytopes by D. Coppersmith and J. Lee The dual active set algorithm and the iterative solution of linear programs by W. W. Hager Positive eigenvalues of generalized words in two Hermitian positive definite matrices by C. J. Hillar and C. R. Johnson Semi-infinite linear programming approaches to semidefinite programming problems by K. Krishnan and J. E. Mitchell SDP versus LP relaxations for polynomial programming by J. B. Lasserre An approximation scheme for the rectilinear Steiner minimum tree in presence of obstructions by M. Min, S. C.-H. Huang, J. Liu, E. Shragowitz, W. Wu, Y. Zhao, and Y. Zhao A convex feasibility problem defined by a nonlinear separation oracle by F. S. Mokhtarian Efficient algorithms for the smallest enclosing ball problem in high dimensional space by G. Zhou, J. Sun, and K.-C. Toh.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".