On character varieties, sets of discrete characters, and nonzero degree maps
Bibliographic record
Abstract
A {\it knot manifold} is a compact, connected, irreducible, orientable $3$-manifold whose boundary is an incompressible torus. We first investigate virtual epimorphisms between the fundamental groups of small knot manifolds and prove minimality results for small knot manifolds with respect to nonzero degree maps. These results are applied later in the paper where we fix a small knot manifold $M$ and investigate various sets of characters of representations $\rho: \pi_1(M) \to {\rm PSL}_2(\Bbb{C})$ whose images are discrete. We show that the topology of these sets is intimately related to the algebraic structure of the ${\rm PSL}_2(\Bbb{C})$-character variety of $M$ as well as dominations of manifolds by $M$ and its Dehn fillings. We apply our results to the following question of Shicheng Wang: {\it Are nonzero degree maps between infinitely many distinct Dehn fillings of two hyperbolic knot manifolds $M$ and $N$ induced by a nonzero degree map $M \to N$?} We show that the answer is yes generically. Using this we show that if a small $\mathcal{H}$-minimal hyperbolic knot manifold admits non-homeomorphic $\mathcal{H}$-minimal Dehn fillings, it admits infinitely many such fillings. We also construct the first infinite families of small, closed, connected, orientable manifolds which are minimal in the sense that they do not admit nonzero degree maps, other than homotopy equivalences, to any aspherical manifold.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".