My scientific genealogy and the Toronto ACDC Laboratory, 1988–2022
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
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.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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