Bibliographic record
Abstract
1Translational Addiction Research Laboratory, Centre for Addiction and Mental Health, University of Toronto, ON, Canada 2Acute Care Program, Centre for Addiction and Mental Health, Toronto, ON, Canada 3Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada. 4Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada 5Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada 6Department of Psychiatry, Division of Neurosciences and Clinical Translation, University of Toronto, Toronto, ON, Canada 7Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada Corresponding Author: Bernard Le Foll, MD, PhD, MCFP, AM, Centre for Addiction and Mental Health, 33 Ursula Franlin Street, Toronto, Ontario, M5S 2S1, Canada. Tel: +1 416 535 8501; E-mail: [email protected] Disclosures: Dr Le Foll has obtained funding from Pfizer (GRAND Awards, including salary support) for investigator-initiated projects. Dr Le Foll has some in-kind donation of cannabis product from Aurora and medication donation from Pfizer and Bioprojet and was provided a coil for TMS study from Brainsway. Dr Le Foll has obtained industry funding from Canopy (through research grants handled by CAMH or University of Toronto), Bioprojet, ACS, and Alkermes. Dr Le Foll has received in kind donations of nabiximols from GW Pharma for past studies funded by CIHR and NIH.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".