Bibliographic record
Abstract
Geometric methods: Mixed toric residues and Calabi-Yau complete intersections by V. V. Batyrev and E. N. Materov Crepant resolutions of $\mathbb{C}^n/A_1(n)$ and flops of $n$-folders for $n=4,5$ by L. Chiang and S.-s. Roan Picard-Fuchs equations, integrable systems and higher algebraic K-theory by P. L. del Angel and S. Muller-Stach Counting BPS states via holomorphic anomaly equations by S. Hosono Regulators of Chow cycles on Calabi-Yau varieties by J. D. Lewis Arithmetic methods: Calabi-Yau manifolds over finite fields, II by P. Candelas, X. de la Ossa, and F. Rodriguez-Villegas Modularity of rigid Calabi-Yau threefolds over $\mathbb{Q}$ by L. Dieulefait and J. Manoharmayum $K3$ surfaces with symplectic group actions by Y. Goto Birational smooth minimal models have equal Hodge numbers in all dimensions by T. Ito The $n$th root of the mirror map by B. H. Lian and S.-T. Yau On a Shioda-Inose structure of a family of K3 surfaces by L. Long Black hole attractor varieties and complex multiplication by M. Lynker, V. Periwal, and R. Schimmrigk Hypergeometric families of Calabi-Yau manifolds by F. Rodriguez-Villegas Aspects of conformal field theory from Calabi-Yau arithmetic by R. Schimmrigk Ordinary Calabi-Yau-3 crystals by J. Stienstra The ordinary limit for varieties over $\mathbb{Z}[x_1,\ldots,x_r]$ by J. Stienstra Update on the modularity of Calabi-Yau varieties with appendix by Helena Verrill by N. Yui Problems by N. Yui and J. D. Lewis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".