The 2002 George W. Beadle Medal Essay: Why I Developed the Yeast Genetic Map
Bibliographic record
Abstract
IN the late 1940s while finishing my degree as an honors physics major at the University of Alberta, I decided to look for future options. Fortunately, UA had a good library where I discovered the program in biophysics at the University of California, Berkeley. I wrote to Professor Cornelius Tobias, advisor of the program, and was accepted as a Ph.D. candidate with a teaching assistantship. Meanwhile, I had begun doing geophysical work with an oil survey company in Calgary at a time when large reservoirs of oil were being discovered in Alberta. The oil industry offered an adventuresome and lucrative future. However, I opted for UC Berkeley and was married; together we headed south where I joined Tobias, who was working with Raymond Zirkle, professor at the University of Chicago, trying to determine if haploid cells were more or less resistant to ionizing radiation than diploid cells. The diploids were more resistant. Zirkle and Tobias constructed a recessive lethal model to explain this difference. To test their model I constructed and tested triploid and tetraploid cells, which, contrary to their model that predicted that they should be more resistant, became progressively more sensitive. After perusing the biology library at Berkeley regarding radiobiology and ploidy, I found that there was much to learn about the field. Subsequently, my interests focused on the area of dominant lethality, and after further research, I proposed a theory to explain the higher sensitivity in tetraploid cells: the damage was partially caused by dominant lethality, which resulted from chromosome aberrations whose frequencies increased with the ploidy of the irradiated cell. Such damage was then demonstrated in haploid and diploid irradiated cells in yeast, thus confirming my hypothesis.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".