Genomics-based taxonomy to clarify cannabis classification
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".