Author Correction: Psilocybin microdosers demonstrate greater observed improvements in mood and mental health at one month relative to non-microdosing controls
Bibliographic record
Abstract
Authors and Affiliations Department of Psychology, University of British Columbia, Kelowna, BC, Canada Joseph M. Rootman & Zach Walsh Quantified Citizen Technologies Inc., Vancouver, BC, Canada Maggie Kiraga, Kalin Harvey & Eesmyal Santos-Brault Department of Family Medicine, University of British Columbia, Vancouver, BC, Canada Pamela Kryskow Fungi Perfecti, LLC, MycoMedica Life Sciences, Olympia, WA, USA Paul Stamets Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands Maggie Kiraga & Kim P. C. Kuypers Authors Joseph M. Rootman View author publications You can also search for this author in PubMed Google Scholar Maggie Kiraga View author publications You can also search for this author in PubMed Google Scholar Pamela Kryskow View author publications You can also search for this author in PubMed Google Scholar Kalin Harvey View author publications You can also search for this author in PubMed Google Scholar Paul Stamets View author publications You can also search for this author in PubMed Google Scholar Eesmyal Santos-Brault View author publications You can also search for this author in PubMed Google Scholar Kim P. C. Kuypers View author publications You can also search for this author in PubMed Google Scholar Zach Walsh View author publications You can also search for this author in PubMed Google Scholar Corresponding author Correspondence to Joseph M. Rootman .
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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 | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.033 |
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".