Bibliographic record
Abstract
espanolEste articulo hace una resena del libro «Aventuras de un matematico. Memorias de Stanislaw M. Ulam». Se trata de una autobiografia del matematico polaco Stanislaw M. Ulam, uno de los matematicos mas brillantes del siglo XX, fallecido en mayo de 1984. Ulam fue uno de los cientificos que intervino en el Proyecto Manhattan, nombre en clave de un proyecto cientifico llevado a cabo durante la Segunda Guerra Mundial por los Estados Unidos con ayuda del Reino Unido y Canada. El objetivo final del proyecto era el desarrollo de la primera bomba atomica antes de que la Alemania nazi la consiguiera. Ulam realizo diversas aportaciones a las matematicas, entre las que destaca el desarrollo con John Von Neumann del Metodo de Montecarlo EnglishThis article is a review of the book «Adventures of a mathematician». The book is a biography of the Polish mathematician Stanislaw M. Ulam, one of the most brilliant mathematicians of the twentieth century, who died in May 1984. Ulam was one of the scientists who participated in the Manhattan Project, the codename of a scientific project carried out during World War II by the United States with the help of the United Kingdom and Canada. The final objective of the project was the development of the first atomic bomb before Nazi Germany got it. Ulam made several contributions to mathematics, most notably the development of the Monte Carlo method with John Von Neumann.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".