Oliver Smithies - Sources from OLIVER SMITHIES. 23 June 1925 — 10 January 2017
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.211 | 0.011 |
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 it