Expansion in ${\boldsymbol{n^{-1}}}$ for Percolation Critical Values on the $n$-cube and ${\boldsymbol{{\mathbb Z}^n}}$: the First Three Terms
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Bibliographic record
Abstract
Let $p_c({\\mathbb Q}_n)$ and $p_c({\\mathbb Z}^n)$ denote the critical values for nearest-neighbour bond percolation on the $n$-cube ${\\mathbb Q}_n = \\{0,1\\}^n$ and on ${\\mathbb Z}^n$, respectively. Let $\\Omega = n$ for ${\\mathbb G} = {\\mathbb Q}_n$ and $\\Omega = 2n$ for ${\\mathbb G} = {\\mathbb Z}^n$ denote the degree of ${\\mathbb G}$. We use the lace expansion to prove that for both ${\\mathbb G} = {\\mathbb Q}_n$ and ${\\mathbb G} = {\\mathbb Z}^n$, \\[p_c({\\mathbb G}) = \\Omega^{-1} + \\Omega^{-2} + \\frac{7}{2} \\Omega^{-3} + O(\\Omega^{-4}).\\] This extends by two terms the result $p_c({\\mathbb Q}_n) = \\Omega^{-1} + O(\\Omega^{-2})$ of Borgs, Chayes, van der Hofstad, Slade and Spencer, and provides a simplified proof of a previous result of Hara and Slade for ${\\mathbb Z}^n$.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it