MétaCan
Menu
Back to cohort
Record W4376108671 · doi:10.1139/gen-2023-0005

Genomics-based taxonomy to clarify cannabis classification

2023· review· en· W4376108671 on OpenAlexafffundvenue
Éliana Lapierre, Adrian S. Monthony, Davoud Torkamaneh

Bibliographic record

VenueGenome · 2023
Typereview
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversity College Dublin
KeywordsBiologyTaxonomy (biology)CannabisConfusionSubspeciesNomenclatureContext (archaeology)Evolutionary biologyGenomicsZoologyGenomeGeneticsGenePsychologyPaleontology

Abstract

fetched live from OpenAlex

In the 18th century, Carolus Linnaeus created a formalized system of classification of living organisms based on their anatomic relationships, which we know as taxonomic nomenclature. Historically, the genus Cannabis has been described three ways under this system: Cannabis sativa by C. Linnaeus in 1753, Cannabis indica by J.B. Lamarck in 1785, and Cannabis ruderalis by D.E. Janischewsky in 1924, with these taxonomic classifications having been derived from physical, morphological, chemical, and geographical data. Today, this confusing taxonomy has led to an ongoing debate about whether the genus Cannabis consists of a single species or multiple distinct species or subspecies. Recently, genome sequencing and bioinformatics have provided greater resolution of taxonomic assignments at the species level. As a result, some previously discussed classification frameworks have been brought into question. The aim of this review is to provide a historical context for the confusion surrounding the taxonomy of the genus Cannabis and highlight recent research on genomics-based taxonomical approaches to clarify the question of Cannabis taxonomy. We suggest that the latest evidence shifts away from the previous multiple species framework and points towards the genus Cannabis consisting of a highly diverse monotypic species.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.159
GPT teacher head0.348
Teacher spread0.190 · 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 teacher head, not a consensus.

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

Citations52
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueGenomeSame topicBiochemical Analysis and Sensing TechniquesFrench-language works237,207