Bibliographic record
Abstract
In this thesis we present two different, but related, results; one in the setting of Teichmuller theory, and the other in the setting of negative curvature. For the first result, let $\gamma$ be a pseudo-Anosov homeomorphism of a compact orientable surface $S_g$, and let $L_\gamma$ denote the axis of the action of $\gamma$ on the Teichmuller space of $S_g$, denoted by $\mathcal{T}_g$. In Chapter 3 we obtain asymptotics for the number of translates of $L_\gamma$ that intersect a Teichmuller ball of radius $R$ centered at a fixed $X \in \mathcal{T}_g$, as $R \rightarrow \infty$. For the second result, let $M$ be a compact closed manifold of variable negative curvature. We fix two points $x, y$ in the universal cover $\widetilde{M}$ of $M$, fix an element $\mathrm{id} \neq \gamma$ in the fundamental group $\Gamma$ of $M$, and denote the set of elements in $\Gamma$ that are conjugate to $\gamma$ by $\mathrm{Conj}_\gamma$. In Chapter 4 we obtain asymptotics for the number of $\mathrm{Conj}_\gamma$--orbits of $y$ that lie in a ball of radius $R$ centered at $x$, as $R \rightarrow \infty$. If $M$ is two-dimensional, or of dimension $n \geq 3$ and curvature bounded above by $-1$ and below by $-(\frac{n-1}{n-2})^2$, we find a power saving error term for this count. Since the two results are written in different settings, their similarities might be hidden at first glance. This is why we included Chapter 2, in which we present a unified approach to both results in the setting of constant negative curvature. Writing the arguments in this simple setting helps us emphasize the similarities between the two results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".