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Record W4399776105 · doi:10.1503/jpn.240049

The <i>Cannabis Act</i>: implications for human participant research with cannabis

2024· editorial· en· W4399776105 on OpenAlexaffvenueabout
Patricia Di Ciano, Christine M. Wickens, Elvin M. Paul, Raesham Mahmood, Jean‐François Crépault, Sergio Rueda, Isabelle Boileau

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2024
Typeeditorial
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsMental Health Research CanadaPublic Health OntarioUniversity of TorontoInstitute for Work & HealthCentre for Addiction and Mental Health
Fundersnot available
KeywordsCannabisEffects of cannabisPsychiatryPsychologyMedicineCannabidiol

Abstract

fetched live from OpenAlex

In Canada, cannabis was legalized for medical purposes in 2001 and for nonmedical use in 2018, with edibles, concentrates, and topicals following in 2019 under the Canadian Cannabis Act (Bill C-45).[1][1] This legislation regulates the production, distribution, sale, and possession of cannabis,

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.047
metaresearch head score (Gemma)0.136
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.991
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.136
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0050.002
Science and technology studies0.0080.010
Scholarly communication0.0120.008
Open science0.0070.003
Research integrity0.0350.035
Insufficient payload (model declined to judge)0.0120.012

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.

Opus teacher head0.072
GPT teacher head0.424
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
GenreEditorial

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".

Quick stats

Citations1
Published2024
Admission routes3
Has abstractyes

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