Bibliographic record
Abstract
Introduction Amongst the Hungarian mathematicians, Jansci von Neumann stood out. From a young age, there were stories of strange abilities: dividing two eight-digit numbers in his head at six; proficient in calculus at eight; reading Borel's Théorie des Fonctions at twelve. Stories abound about a photographic memory and an ability to apparently recall complete novels and pages of the telephone directory. He also accumulated an encyclopaedic knowledge of history, in time being able to recall the most minute details of the Peloponnesian Wars, the trial of Joan of Arc, and Byzantine history. Many years later in the U.S., when travelling south from Princeton, New Jersey, to Duke University, North Carolina, he astounded his fellow travellers, including mathematicians Albert Tucker and Stan Ulam, with his recollection of the most precise details of Civil War battles fought at sites along the route. Although Max von Neumann would have preferred his son to become a well-paid financier rather than a mathematician, he was open to the encouragements of Fejér and Ortvay and finally acquiesced, letting von Neumann pursue his interests and financing his studies abroad. Von Neumann, in return, became the shining, often absent, star of the Fejér circle in Budapest. As a Gymnasium student, he caught the attention of Laszló Rátz and was tutored in university-level mathematics by Mikhail Fekete. By the time he enrolled at the University of Budapest in 1921, he had already written a paper with Fekete and, according to Ulam, was essentially recognized as a mathematician.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.210 | 0.091 |
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".