Phenotypic Characterization of a Diverse Population of <em>Cannabis sativa</em> for Agronomic, Morphological, and Biochemical Traits
Bibliographic record
Abstract
Cannabis (Cannabis sativa L.) stands as a historically significant and culturally important plant, embodying economic, social, and medicinal relevance for human societies. However, years of prohibition and stigmatization have hindered the cannabis research community, which is hugely undersized and suffers from a scarcity of understanding of cannabis genetics and how key traits are expressed or inherited. In this study, we conducted a comprehensive phenotypic characteriza-tion of 176 drug-type cannabis accessions, representative of Canada's legal market. We assessed germination methods, evaluated various traits including agronomic, morphological, and canna-binoid profiles, and uncovered significant variation within this population. Notably, yield dis-played a negative correlations with maturity-related traits but positive correlation with fresh biomass. Additionally, the potential THC content showed positive correlation with maturi-ty-related traits but negative correlation with yield. Significant different were observed between plants derived from regular female seeds and feminized seeds, as well as between plants derived from cuttings and seeds for different traits. This study advances our understanding of cannabis cultivation, offering insights into germination practices, agronomic traits, morphological charac-teristics, and biochemical diversity. These findings establish a foundation for precise breeding and cultivar development, enhancing cannabis's potential in the legal market.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".