An interview with Professor Gregory Steinberg, Co-Director of the Centre for Metabolism, Obesity and Diabetes Research and the Canada Research Chair in Metabolism and Obesity at McMaster University
Bibliographic record
Abstract
Gregory Steinberg completed his PhD at the University of Guelph, Canada, where he studied the role of leptin in muscle in the laboratory of Professor David Dyck. From 2002 to 2006, Greg was a postdoctoral fellow with Professor Bruce Kemp at St Vincent’s Institute of Medical Research, Australia, where he studied the role of the AMP-activated protein kinase (AMPK). In 2006, he started his academic career as Lecturer, Senior Research Fellow and Head of Metabolism at St Vincent’s Institute and the University of Melbourne. He returned to Canada in 2009 where his research studies cellular energy sensing mechanisms and looks at how endocrine factors, lipid metabolism and insulin sensitivity are linked and contribute to the development of obesity, type 2 diabetes, cardiovascular disease and cancer. He is the recipient of numerous awards, including the Diabetes Canada-Canadian Institutes of Health Research, Diabetes Young Scientist Award; the Endocrine Society Richard E Weitzman Outstanding Early Career Investigator Award; the Canadian Institutes of Health Research Gold Leaf Prize; and the American Diabetes Association Outstanding Scientific Achievement Award. To help celebrate the 100th anniversary of the discovery of insulin, Greg also recently took part in Diabetes Canada’s fitness fundraiser, ‘Lace Up for Diabetes’, riding 8000 km (the equivalent of crossing Canada) over a period of 80 days and raising almost $15,000 to help fund the next breakthrough discovery in the field. Coinciding with our issue theme of diabetes, which also marks the centenary of this discovery by Canadian scientists, The Biochemist spoke to Greg briefly about his work in this area.
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 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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".