The E. Donnall Thomas lecture apoptosis: “’Tis death that makes life live”
Bibliographic record
Abstract
Dr. Tak Mak was this year’s honored E. Donnall Thomas lecturer at the Tandem BMT Meetings, held on February 1, 2003, in Keystone, Colorado. Dr. Mak has contributed a cornerstone of immunology by elucidating the structure and organization of the T-cell receptor (TCR), a contribution of vital importance to many, if not all, of us in this field. His work now extends in exciting new directions involving signaling pathways in apoptosis. Dr. Mak is consistently identified by Science Watch as one of the most cited scientists in the world. He completed his undergraduate studies at the University of Wisconsin in Madison and obtained his PhD degree from the University of Alberta. Currently, he is the director of the Advanced Medical Discovery Institute in Toronto and is a senior scientist of the Ontario Cancer Institute. During his very distinguished career, Dr. Mak has garnered many awards, including the Gairdner International Award and the General Motors Sloan Prize. He is a member of the National Academy of Sciences, a rare distinction for non-Americans, and a Fellow of the Royal Society. The following review is a modified transcribed version of the presentation made by Dr. Mak.
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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 | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".