Randomization, relaxation, and complexity in polynomial equation solving : Banff International Research Station workshop on Randomization, Relaxation, and Complexity, February 28-March 5, 2010, Banff, Ontario, Canada
Bibliographic record
Abstract
Multivariate ultrametric root counting by M. Avendano and A. Ibrahim A parallel endgame by D. J. Bates, J. D. Hauenstein, and A. J. Sommese Efficient polynomial system solving by numerical methods by C. Beltran and L. M. Pardo Symmetric determinantal representation of formulas and weakly skew circuits by B. Grenet, E. L. Kaltofen, P. Koiran, and N. Portier Mixed volume computation in solving polynomial systems by T.-L. Lee and T.-Y. Li A search for an optimal start system for numerical homotopy continuation by A. Leykin Complex tropical localization, and coamoebas of complex algebraic hypersurfaces by M. Nisse Randomization, sums of squares, near-circuits, and faster real root counting by O. Bastani, C. J. Hillar, D. Popov, and J. M. Rojas Dense fewnomials by K. Rusek, J. Shakalli, and F. Sottile The numerical greatest common divisor of univariate polynomials by Z. Zeng
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".