The Random Walk Path of Pál Révész in Probability
Bibliographic record
Abstract
Pl Rvsz was a world-renowned Hungarian probabilist and an extremely prolific mathematician, having written around 200 research papers and four books. A graduate of Etvs Lrnd University, Rvsz spent decades as the Head of the Probability Department of the Rnyi Institute before heading the Department of Statistics and Probability of Vienna University of Technology. He was also a visiting professor at numerous universities across Europe and Canada. He was elected to be a member of the Hungarian Academy in 1982, and he served as the president of the Bernoulli Society of Mathematical Statistics and Probability from 1983 to 1985, as well as becoming a member of the Academy Europaea in 1991. He was beyond generous in his collaborations, always happy to talk about the problems he was working on. He listened with the same respect and curiosity whether talking to a famous professor or an eager student. He loved being able to help a new generation of mathematicians. Besides mathematics, he loved classical music, long walks, and the company of friendsbut he admitted that he was still doing mathematics in his head during these concerts and long walks. In this volume, we have collected papers from his coworkers, friends, and colleagues to commemorate his life and everlasting impact on probability theory.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".