Bibliographic record
Abstract
How long does a self-avoiding walk on a discrete $d$-dimensional torus have to be before it begins to behave differently from a self-avoiding walk on $\mathbb{Z}^d$? We consider a version of this question for weakly self-avoiding walk on a torus in dimensions $d>4$. On $\mathbb{Z}^d$ for $d>4$, the partition function for $n$-step weakly self-avoiding walk is known to be asymptotically purely exponential, of the form $A\mu^n$, where $\mu$ is the growth constant for weakly self-avoiding walk on $\mathbb{Z}^d$. We prove the identical asymptotic behaviour $A\mu^n$ on the torus (with the same $A$ and $\mu$ as on $\mathbb{Z}^d$) until $n$ reaches order $V^{1/2}$, where $V$ is the number of vertices in the torus. This shows that the walk must have length of order at least $V^{1/2}$ before it "feels" the torus in its leading asymptotics. Our results support the conjecture that the behaviour of the partition function does change once $n$ reaches $V^{1/2}$, and we relate this to a conjectural critical scaling window which separates the dilute phase $n \ll V^{1/2}$ from the dense phase $n \gg V^{1/2}$. To prove the conjecture and to establish the existence of the scaling window remains a challenging open problem. The proof uses a novel lace expansion analysis based on the "plateau" for the torus two-point function obtained in previous work.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".