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
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
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.
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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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