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Record W2277659540

Cannabidiol, a non-intoxicating component of cannabis, suppresses nausea and vomiting in rats and shrews

2013· article· en· W2277659540 on OpenAlexaffabout
Erin M. Rock, Daniele Bolognini, Cheryl L. Limebeer, M. G. Cascio, Sharon Anavi‐Goffer, P.J. Fletcher, Raphael Mechoulam, Roger G. Pertwee, Lucy Anne Parker

Bibliographic record

VenueThe Atrium (University of Guelph) · 2013
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCannabidiolSerotoninNauseaVomitingPharmacologyCannabinoid receptorMedicineNeuroscienceAnesthesiaPsychologyCannabisReceptorInternal medicineAgonistPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

This summary is a project of the Institute for Community Engaged \nScholarship (ICES) at the University of Guelph, with project partners: \nthe Catalyst Centre, SPARK Program at the \nUniversity of Guelph, and Knowledge Mobilization Unit at York \nUniversity. This project is part of the Pan-Canadian Research \nImpact Network. http://csahs.uoguelph.ca/pps/Clear_Research

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2013
Admission routes2
Has abstractyes

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