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Terpenes in Cannabis sativa – From plant genome to humans

2019· review· en· W2927423910 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePlant Science · 2019
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGenome British Columbia
KeywordsCannabisCannabis sativaBiologyTerpeneCannabinoidBiotechnologyBotanyPsychiatryPsychologyBiochemistry

Abstract

fetched live from OpenAlex

Cannabis sativa (cannabis) produces a resin that is valued for its psychoactive and medicinal properties. Despite being the foundation of a multi-billion dollar global industry, scientific knowledge and research on cannabis is lagging behind compared to other high-value crops. This is largely due to legal restrictions that have prevented many researchers from studying cannabis, its products, and their effects in humans. Cannabis resin contains hundreds of different terpene and cannabinoid metabolites. Many of these metabolites have not been conclusively identified. Our understanding of the genomic and biosynthetic systems of these metabolites in cannabis, and the factors that affect their variability, is rudimentary. As a consequence, there is concern about lack of consistency with regard to the terpene and cannabinoid composition of different cannabis 'strains'. Likewise, claims of some of the medicinal properties attributed to cannabis metabolites would benefit from thorough scientific validation.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.368
Teacher spread0.296 · 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