My scientific genealogy and the Toronto ACDC Laboratory, 1988–2022
Bibliographic record
Abstract
There is a saying that as people get older, they prefer to speak more about the past and less about the future. As I go through the last chapter of my scientific career, which spans from 1988-2022, I traced my scientific genealogy and the most important scientific achievements of my laboratory. By examining close to 1,000 PubMed-indexed papers published, I found out that none of them describes best our most important contributions. Also, by realizing that our contributions in science would have likely been discovered by others shortly afterwards, I focused my attention to other metrics. I suggest here that the best metric of success is the number of people that have been trained in my lab, and found their own way in their professional and other endeavors. Over the years, I trained over 250 individuals, of which 49 obtained a PhD, 19 an MSc, 37 were post-doctoral fellows, 5 were clinical fellows and about 150 were co-op/undergraduates and summer students. Many of these individuals now hold important positions in Academia, Government and Industry. My graduates, who have now created their own genealogy and many more individuals with roots to my laboratory, are now serving the society. In conclusion, I consider the development of young trainees as my most important career contribution.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.013 |
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".