Spatio-Temporal and Cultivar-Dependent Variations in the Cannabis Microbiome
Bibliographic record
Abstract
The incipient legalization and commercialization of Cannabis sativa in Canada have promulgated research into characterizing the plant’s microbiome as it promotes many facets of plant growth and health. The emblematic production of commercially important secondary metabolites, namely tetrahydrocannabinol (THC), cannabidiol (CBD) and terpenes, has warranted investigating the modulating capacity of these molecules on the plant microbiome. C. sativa cultivars can be classified into chemotypes depending on the relative levels of THC and CBD they produce; their biosynthesis also varies spatially and temporally during the life cycle of the plant. To study the differential microbiome structure and diversity between cultivars in a spatio-temporal manner, we extracted microbial DNA from the rhizosphere, endorhizosphere and phyllosphere during the entire life cycle of three different chemotypes; CBD Yummy (<1% THC/13% CBD), CBD shark (6% THC/10% CBD) and Hash plant (14% THC/<1% CBD). Illumina marker gene sequencing of bacterial (16S) and fungal (ITS) communities were coupled to the QIIME2, PICRUSt and LEfSe pipelines for analysis. Our study describes spatio-temporal and cultivar-dependent variations in the fungal and bacterial microbiome of C. sativa, and details strong cultivar-dependent variance in the belowground microbiome. Furthermore, the predicted pathway abundance of the bacterial microbiome is concomitantly subject to spatio-temporal variations; pathways related to lipid, amino acid, glucose and pentose metabolism were noteworthy. These results describe, for the first time, spatio-temporal and cultivar-dependent variations in the microbiome of C. sativa produced under strict commercial settings. Describing the microbiome is the first step in discoveries that could help in engineering a plant growth and health promoting microbiome in future works.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".