Subcritical Gaussian multiplicative chaos in the Wiener space: construction, moments and volume decay
Bibliographic record
Abstract
Abstract We construct and study properties of an infinite dimensional analog of Kahane’s theory of Gaussian multiplicative chaos (Kahane in Ann Sci Math Quebec 9(2):105-150, 1985). Namely, if $$H_T(\omega )$$ H T ( ω ) is a random field defined w.r.t. space-time white noise $$\dot{B}$$ B ˙ and integrated w.r.t. Brownian paths in $$d\ge 3$$ d ≥ 3 , we consider the renormalized exponential $$\mu _{\gamma ,T}$$ μ γ , T , weighted w.r.t. the Wiener measure $$\mathbb {P}_0(\textrm{d}\omega )$$ P 0 ( d ω ) . We construct the almost sure limit $$\mu _\gamma = \lim _{T\rightarrow \infty } \mu _{\gamma ,T}$$ μ γ = lim T → ∞ μ γ , T in the entire weak disorder (subcritical) regime and call it subcritical GMC on the Wiener space. We show that $$\begin{aligned} \mu _\gamma \Big \{\omega : \lim _{T\rightarrow \infty } \frac{H_T(\omega )}{T(\phi \star \phi )(0)} \ne \gamma \Big \}=0 \qquad \text{ almost } \text{ surely, } \end{aligned}$$ μ γ { ω : lim T → ∞ H T ( ω ) T ( ϕ ⋆ ϕ ) ( 0 ) ≠ γ } = 0 almost surely, meaning that $$\mu _\gamma $$ μ γ is supported almost surely only on $$\gamma $$ γ -thick paths, and consequently, the normalized version is singular w.r.t. the Wiener measure. We then characterize uniquely the limit $$\mu _\gamma $$ μ γ w.r.t. the mollification scheme $$\phi $$ ϕ in the sense of Shamov (J Funct Anal 270:3224–3261, 2016) – we show that the law of $$\dot{B}$$ B ˙ under the random rooted measure $$\mathbb Q_{\mu _\gamma }(\textrm{d}\dot{B}\textrm{d}\omega )= \mu _\gamma (\textrm{d}\omega ,\dot{B})P(\textrm{d}\dot{B})$$ Q μ γ ( d B ˙ d ω
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".