Hi Reddit, I'm Warren Chan of the University of Toronto. Ask me anything about applying nanotechnology to treating cancer and infectious diseases. AMA!
Bibliographic record
Abstract
ACS AMA Hello Reddit! My name is Warren Chan, and I am currently Distinguished Professor of Biomedical Engineering at The University of Toronto. I also serve as Associate Editor of ACS Nano. I am very much looking forward to my first time participating in Reddit. I obtained my B.S. from University of Illinois in 1996 and a PhD from Indiana University in 2001, both in Chemistry. Then I did my post-doctoral work at the University of California-San Diego in Biomedical Engineering and I joined the faculty at the University of Toronto in 2002 at the Institute of Biomaterials and Biomedical Engineering. I am interested in developing nanotechnology for diagnosing and treating cancer and infectious diseases. As a chemist, I learned how to make and design nanomaterials and as I started my independent career, I wanted to focus on applying these materials to the medical field. My interest can span two domains: (a) outside of the body, I am interested in developing handheld nanotechnology devices that can identify biomarkers and link them to diseases. These devices can also measure these biomarkers with a single drop of blood. (b) inside the body, I am interested in figuring out how to deliver nanoparticles to the diseased site. I think the biggest challenge of using nanotechnology is to be able to deliver enough of the medical agent to the site of action. I work with engineers, chemists, biologists, and clinicians to solve these problems. I would like forward to our discussion. Ask me anything about bionanotechnology! I’ll be back at 11am EDT (8am PDT, 3pm UTC) to start answering your questions. It has been awesome chatting with everybody on nanotechnology! I am signing off! Have an awesome day!
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.180 | 0.074 |
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".