Oliver Smithies - Bibliography from OLIVER SMITHIES. 23 June 1925 — 10 January 2017
Bibliographic record
Abstract
Oliver Smithies was born in Copley, near Halifax in Yorkshire, UK. He received his doctorate from Oxford in 1951, then began working at the Connaught Laboratories in Toronto, where he developed starch gel electrophoresis. This technology allowed identification of genetic variants in human serum proteins and revolutionized protein analysis. After moving to the University of Wisconsin, he studied the genetics of antibody variability, then turned to nucleic acid methods, developing safe cloning vectors, driving production of software for genetic analysis, sequencing several human genes and finally creating genetically engineered animals, for which he later received the Nobel Prize. He then moved to the University of North Carolina, where he developed methods for altering gene dosage in mice, which he used to develop ways to attack complex physiological questions, including blood pressure regulation. Finally, he formulated a new hypothesis to explain kidney glomerular filtration, then devised methods that confirmed the hypothesis. Oliver collaborated with his wife, Nobuyo Maeda, during a long and happy marriage. While maintaining separate laboratories, they stimulated each other's scientific understanding and frequently published together. During a 70-year life in science, he mentored many students, postdoctoral fellows and collaborators, nearly all remaining his friends for life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.221 | 0.124 |
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".