Validating a predictive model of cannabinoid inheritance with feral, clinical, and industrial <i>Cannabis sativa</i>
Bibliographic record
Abstract
PREMISE: How genetic variation within a species affects phytochemical composition is a fundamental question in botany. The ratio of two specialized metabolites in Cannabis sativa, tetrahydrocannabinol (THC) and cannabidiol (CBD), can be grouped into three main classes (THC-type, CBD-type, and intermediate type). We tested a genetic model associating these three groups with functional and nonfunctional alleles of the cannabidiolic acid synthase gene (CBDAS). METHODS: We characterized cannabinoid content and assayed CBDAS genotypes of >300 feral C. sativa plants in Minnesota, United States. We performed a test cross to assess CBDAS inheritance. Twenty clinical cultivars obtained blindly from the National Institute on Drug Abuse and 12 Canadian-certified grain cultivars were also examined. RESULTS: Frequencies of CBD-type, intermediate-type, and THC-type feral plants were 0.88, 0.11, and 0.01, respectively. Although total cannabinoid content varied substantially, the three groupings were perfectly correlated with CBDAS genotypes. Genotype frequencies observed in the test cross were consistent with codominant Mendelian inheritance of the THC:CBD ratio. Despite significant mean differences in total cannabinoid content, CBDAS genotypes blindly predicted the THC:CBD ratio among clinical cultivars, and the same was true for industrial grain cultivars when plants exhibited >0.5% total cannabinoid content. CONCLUSIONS: Our results extend the generality of the inheritance model for THC:CBD to diverse C. sativa accessions and demonstrate that CBDAS genotyping can predict the ratio in a variety of practical applications. Cannabinoid profiles and associated CBDAS segregation patterns suggest that feral C. sativa populations are potentially valuable experimental systems and sources of germplasm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".