Bibliographic record
Abstract
My career developed very differently from those of most academic researchers. After school, I worked for 6 years in industries that employed yeast to manufacture ethanol and beer. At university, I was trained as a microbiologist with very little training in molecular biology. I retrained in 1987 in molecular yeast genetics and focused on genetic engineering of industrial yeasts to minimize the production of spoilage compounds in wine and ethyl carbamate, a carcinogen, in wine. The malolactic yeast ML01 and the urea-degrading yeast were the first genetically enhanced yeasts that obtained US FDA approval for commercial applications. Apart from applied research, I was fascinated by classic molecular yeast genetic studies using sophisticated techniques such as transcriptomics, proteomics, and metabolomics. Doing research at the University of British Columbia was stimulating and exciting, we established a core microarray and metabolomics facility that was used by many scientists at UBC and hospitals in Vancouver. I also established a state-of-the-art Wine Library that was used to study aging of wines produced in British Columbia. Finally, I have been fortunate to know and collaborate with leading yeast scientists who motivated me.
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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.034 | 0.033 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.073 | 0.034 |
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".