Bibliographic record
Abstract
Barton Bernstein’s review ( Physics Today, May 2007, page 63) of The Martians of Science: Five Physicists Who Changed the Twentieth Century by István Hargittai (Oxford U. Press, 2006) pointedly evaluates Michael Gorn’s 1992 biography of aeronauticist Theodore von Kármán as brief and uncritical. (And I would add, replete with names, many of which add little benefit.) But Gorn, like others thus far, seems to have over-looked John von Neumann’s contribution to aerodynamics. Early attempts to deal numerically with aerodynamic flows that develop shocks ground to a halt in rezoning the shock too finely for computation to proceed. With Robert Richtmyer, von Neumann demonstrated an algorithm for introducing an “artificial viscosity” that sets a lower bound to shock thickness without violating any physics. 1 1. R. D. Richtmyer, J. von Neumann, J. Appl. Phys. 21, 232 (1950). https://doi.org/10.1063/1.1699639 Computational physicists are indebted to these two scientists for much of present-day understanding of such diverse problems as supersonic aerodynamics and supernova explosions. REFERENCESection:ChooseTop of pageREFERENCE <<1. R. D. Richtmyer, J. von Neumann, J. Appl. Phys. 21, 232 (1950). https://doi.org/10.1063/1.1699639 , Google ScholarCrossref, ISI© 2007 American Institute of Physics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".