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Record W2624824890 · doi:10.15173/m.v1i25.861

Medical Marijuana – Airing Out the Smoke of Doubt

2014· article· en· W2624824890 on OpenAlexaffvenueabout
Aneesh Karir

Bibliographic record

VenueThe Meducator · 2014
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSmokeEnvironmental healthMedicineEngineeringWaste management

Abstract

fetched live from OpenAlex

On April 1, 2014, Canada will implement a drastically new set of regulations that will redefine the laws on the production and acquisition of medical marijuana, affecting over 35,000 patients nationwide. Patients will have to exclusively seek commercial vendors for medical marijuana and pay a much higher price compared to the costs of growing marijuana in their residences. This provides several benefits to the Canadian economy and protects against drug abuse, yet worries citizens and users who are trying to minimize costs. The Canadian government will require patients to obtain a prescription from a general physician, rather than a specialist, to gain access to medical marijuana. This new legislation therefore introduces a new source of pressure for general practitioners and makes them reluctant to support the bill. Still, with a plethora of viable solutions, such as increased safety and quality assurance for citizens who are wary about the new system, the commercialization of medical marijuana could prove to be a reasonable decision for the nation’s future.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.425
Threshold uncertainty score0.845

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0080.009
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0150.002

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.

Opus teacher head0.026
GPT teacher head0.335
Teacher spread0.309 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2014
Admission routes3
Has abstractyes

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