Dudley Brian Spalding. 9 January 1923—27 November 2016
Bibliographic record
Abstract
Over a remarkably productive professional life Brian Spalding largely shaped the development of numerical procedures for computing complex turbulent flows. He created a major software company, CHAM, through which the fruits of his group's research could be made available to industry and other research groups across the globe. Thus, he became the outstanding founding figure in the subject now called computational fluid dynamics (CFD). His contributions were by no means limited to strategies for converting systems of non-linear partial differential equations to forms suitable for computer solution; he also brought notable innovations to the physical modelling of combustion, turbulence and two-phase flows. Besides research, he engaged deeply with the research community in heat and mass transfer, becoming a founding editor of two international journals in these areas, and a principal driver behind the creation of the International Centre for Heat and Mass Transfer in Belgrade. He also served as the inaugural scientific chairman of the European Research Community on Flow, Turbulence and Combustion. He led a protracted and ultimately successful campaign to enable Veniamin Levich to leave the Soviet Union to settle in Israel. Outside of his technical work, his principal passions were poetry and the Russian language, which were intertwined in several published volumes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.219 | 0.161 |
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".