Bibliographic record
Abstract
We consider crossbar switching networks with base b (that is, constructed from $b\times b$ crossbar switches), scale k (that is, with $b^k$ inputs, $b^k$ outputs, and $b^k$ links between each consecutive pair of stages), and depth l (that is, with l stages). We assume that the crossbars are interconnected according to the spider-web pattern, whereby two diverging paths reconverge only after at least k stages. We assume that each vertex is independently idle with probability q, the vacancy probability. We assume that $b\ge2$ and the vacancy probability q are fixed, and that k and $l=ck$ tend to infinity with ratio a fixed constant $c > 1$. We consider the linking probability Q (the probability that there exists at least one idle path between a given idle input and a given idle output). In a previous paper [Discrete Appl. Math., 37/38 (1992), pp. 437-450] it was shown that if $c\le2$, then the linking probability Q tends to 0 if $0 < q < q_c$ (where $q_c=1/b^{(c-1)/c}$ is the critical vacancy probability) and tends to $(1-\xi)^2$ (where $\xi$ is the unique solution of the equation $\bigl(1-q(1-x)\bigr)^b=x$ in the range $0 < x < 1$) if $q_c < q < 1$. In this paper we extend this result to all rational $c > 1$. This is done by using generating functions and complex-variable techniques to estimate the second moments of various random variables involved in the analysis of the networks.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".