Interpolating with generalized Assouad dimensions
Bibliographic record
Abstract
Abstract The $$\phi $$ ϕ -Assouad dimensions are a family of dimensions which interpolate between the upper box and Assouad dimensions. They are a generalization of the well-studied Assouad spectrum with a more general form of scale sensitivity that is often closely related to “phase-transition” phenomena in sets. In this article we establish a number of key properties of the $$\phi $$ ϕ -Assouad dimensions which help to clarify their behaviour. We prove for any bounded doubling metric space F and $$\alpha \in {\mathbb {R}}$$ α ∈ R satisfying $$\overline{\textrm{dim}}_{\textrm{B}}F<\alpha \le \textrm{dim}_{\textrm{A}}F$$ dim ¯ B F < α ≤ dim A F that there is a function $$\phi $$ ϕ so that the $$\phi $$ ϕ -Assouad dimension of F is equal to $$\alpha $$ α . We further show that the “upper” variant of the dimension is fully determined by the $$\phi $$ ϕ -Assouad dimension, and that homogeneous Moran sets are in a certain sense generic for these dimensions. Further, we study explicit examples of sets where the Assouad spectrum does not reach the Assouad dimension. We prove a precise formula for the $$\phi $$ ϕ -Assouad dimensions for the boundary of Galton–Watson trees that correspond to a general class of stochastically self-similar sets, including Mandelbrot percolation. The proof of this result combines a sharp large deviations theorem for Galton–Watson processes with bounded offspring distribution and a general Borel–Cantelli-type lemma for infinite structures in random trees. Finally, we obtain results on the $$\phi $$ ϕ -Assouad dimensions of overlapping self-similar sets and decreasing sequences with decreasing gaps.
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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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".