Bibliographic record
Abstract
In [9] the Hausdorff dimension, $d_{f}$, of $\partial \mathcal {R}$, the topological boundary of the range of super-Brownian motion for dimensions $d=2,3$ was found; $d_{f}=4-2\sqrt {2}$ if $d=2$, and $d_{f}=(9-\sqrt {17})/2$ if $d=3$. We will refine these dimension estimates in a number of ways. If $L^{x}$ is the total occupation local time of $d$-dimensional super-Brownian motion, $X$, for $d=2$ and $d=3$, we construct a random measure $\mathcal {L}$, called the boundary local time measure, as a rescaling of $L^{x} e^{-\lambda L^{x}} dx$ as $\lambda \to \infty $, thus confirming a conjecture of [19] and further show that the support of $\mathcal {L}$ equals $\partial \mathcal {R}$. This latter result uses a second construction of a boundary local time $\widetilde {\mathcal {L}}$ given in terms of exit measures and we prove that $\widetilde {\mathcal {L}}=c\mathcal {L}$ a.s. for some constant $c>0$. We derive reasonably explicit first and second moment measures for $\mathcal {L}$ in terms of negative dimensional Bessel processes and use them with the energy method to give a more direct proof of the lower bound of the Hausdorff dimension of $\partial \mathcal {R}$ in [9]. The construction requires a refinement of the $L^{2}$ upper bounds in [19] and [9] to exact $L^{2}$ asymptotics. The methods also refine the left tail bounds for $L^{x}$ in [19] to exact asymptotics. We conjecture that the $d_{f}$-dimensional Minkowski content of $\partial \mathcal {R}$ is equal to the total mass of the boundary local time $\mathcal {L}$ up to some constant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".