Bibliographic record
Abstract
Yunghan Au, PhD, MBA, was born in the United Kingdom and completed his undergraduate degree in biochemistry at Imperial College London. His PhD focused on protein structural biology, involving the use of nuclear magnetic resonance spectroscopy. At Princess Margaret Hospital in Toronto, Yunghan researched as a postdoctoral scientist studying the structures of proteins involved in transcriptional regulation. Following this, he pursued a career in scientific sales back in the United Kingdom for four years. Yunghan went on to complete his MBA at the University of Cambridge in order to further pursue his interest in business. He came back to Canada to work for Lilly, GSK, and then AbbVie Canada in market access and health economics and outcomes research, articulating the value proposition of new drugs to public and private payers. Today, Yunghan serves as the VP of Medical Affairs for the Toronto- based company Swift Medical, which is the leading digital wound care management company, seeking to improve wound healing. Yunghan continues his work to incorporate Artificial Intelligence/ Machine Learning in a smartphone application that enables wound care measurement and visualization.
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 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".