Partially Hyperbolic Dynamics, Laminations, and Teichmüller Flow
Bibliographic record
Abstract
Part I - Partially Hyperbolic Dynamics: How to put a stop to domination once and for all (and what comes afterwards) by L. J. Diaz A survey of partially hyperbolic dynamics by F. R. Hertz, M. A. R. Hertz, and R. Ures From hyperbolicity to dominated splitting by E. R. Pujals Some results on the integrability of the center bundle for partially hyperbolic diffeomorphisms by F. R. Hertz, M. A. R. Hertz, and R. Ures Part II - Teichmuller Dynamics: The $J$-invariant, exceptional surfaces and notions of periodicity by K. Calta and J. Smillie Finiteness results for flat surfaces: A survey and problem list by J. Smillie and B. Weiss Lyapunov exponents of Teichmuller flows by M. Viana Exponential decay of correlations for the Rauzy-Veech-Zorich induction map by A. Avila and A. Bufetov Unique ergodicity of translation flows by Y. Cheung and A. Eskin Combinatorial geometry of generic degenerations of quadratic differentials by H. Masur and A. Zorich Part III-Miscellaneous: Dynamics of foliated spaces in codimension greater than one by A. Bis A survey on minimality of horospheric laminations for rational functions by A. Glutsyuk Thermodynamics of inducing schemes and liftability of measures by Y. Pesin and K. Zhang Pseudo-Anosov maps and Wojtkowski's cone methods by M. Gerber Distribution of orbits for Mobius groups by M. Pollicott.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".