Bibliographic record
Abstract
The hiring of Raoul Bott also paid off immensely for Harvard, although it is fair to say that as a young man, Bott did not exhibit great mathematical flair, nor did he show much academic promise in general.Born in Budapest in 1923-and raised mainly in Slovakia (until his family immigrated to Canada in 1938)-Bott was, at best, a mediocre student throughout childhood.In five years of schooling in Bratislava, Slovakia, he did not earn a single A, except in singing and German.In mathematics, he typically got Cs and the occasional B, which should make him a hero among late bloomers.As a youth of about twelve to fourteen, Bott and a friend had fun playing around with electricity-creating sparks, wiring together fuse boxes, transformers, and vacuum tubes, and, in the process, figuring out how various gadgets work.This experimentation eventually served him well.A mathematician, Bott later explained, is "someone who likes to get to the root of things." 1 Although Bott frequently told his Harvard students that he never would have made it into the school as an undergraduate, he somehow managed to get into McGill University, where he majored in electrical engineering.2 Upon graduating in 1945, he joined the Canadian army but left after four months
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.187 | 0.097 |
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".