Melissa Perreault: Thinking big towards a “complexity science” approach in neuroscience – systems, environment, and whole organism research
Bibliographic record
Abstract
Dr. Melissa Perreault, Professor in the Department of Biomedical Sciences at the University of Guelph and member of the College of New Scholars, Artists, and Scientists in the Royal Society of Canada, is participating in the Genomic Press Interview, sharing her unique insights and experiences. As a neuroscientist and citizen of the Métis Nation of Ontario, Dr. Perreault's work bridges Indigenous perspectives with Western neuroscience, focusing on elucidating sex-specific neurobiological mechanisms underlying neuropsychiatric and neurodevelopmental disorders. Her research aims to identify novel biomarkers and therapeutic targets. At the same time, her advocacy extends to promoting Indigenous representation in STEM fields through initiatives like the Indigenous STEM Mentorship Program at the University of Guelph. Dr. Perreault's efforts also encompass promoting ethical engagement with Indigenous communities in neuroscience research globally, championing the integration of Indigenous knowledge into brain science through international collaborations. Her multifaceted approach to neuroscience, combining rigorous scientific inquiry with cultural sensitivity and inclusivity, positions her at the forefront of a new era in brain research that embraces diverse perspectives and holistic understanding.
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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 | 0.020 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".