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Record W4407977233 · doi:10.36676/978-81-980948-9-6.19

Cannabis – Personalized Medicine: A Review

2025· review· en· W4407977233 on OpenAlexaboutno aff
R. Bharath Kumar

Bibliographic record

Venuenot available
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisPersonalized medicineMedicineTraditional medicinePsychiatryBioinformaticsBiology

Abstract

fetched live from OpenAlex

The hallucinogenic chemical known as cannabis, or marijuana, is extracted from the cannabis plant. It is mostly used for medical, recreational, and sometimes spiritual purposes. Between 128 and 232 million people, or 2.7% to 4.9% of the world's population between the ages of 15 and 65, were reported to have used cannabis as of 2013. Cannabis is the most commonly used illicit substance globally, despite being primarily banned. As of 2018, adult usage rates of the drug were highest in Zambia, the US, Canada, and Nigeria. The cannabis plant's copious lignocellulosic biomass is a valuable renewable resource that can be used to produce energy, textiles, chemicals, and biopolymers. In the bio-composite industry, hemp bast fibers in particular are becoming more and more popular as an eco-friendly substitute for glass fibers. Because hemp bast fibers are stronger and lighter than polypropylene plastic, the automotive industry is particularly interested in using them to make bioplastics.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.052
GPT teacher head0.430
Teacher spread0.378 · 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
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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