Michael Meaney: What is the biology that underlies the gene x environment interdependence that shapes brain health?
Bibliographic record
Abstract
As a distinguished James McGill Professor and now Professor Emeritus at McGill University, Michael Meaney's scientific journey is a testament to the power of curiosity in science. His fascination with how our environment shapes our genes, brain function, and mental health has led to discoveries that have changed how we think about human development. After leading groundbreaking research at McGill, he took his expertise to Singapore, where, as Director of the Translational Neuroscience program at ASTAR, he helped shape the innovative GUSTO birth cohort study. His profound impact on neuroscience is reflected not just in his impressive collection of honors – from the Order of Canada to his recent election to the American Academy of Arts and Sciences – but in how his work has touched lives. With over 650 publications to his name, Meaney has helped bridge the gap between molecular biology and public health. We are fortunate to have him share his insights with our readers in this Genomic Press Interview.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".