Effects of Nitric oxide Producing Bacteria Azospirillum brasilense on Microbial Composition and Secondary Metabolite Profile of Cannabis
Bibliographic record
Abstract
Due to the upcoming legalization of Cannabis sativa, new research opportunities have arisen allowing for the study of its unique assortment of secondary metabolites. While the biosynthetic pathways leading to the production of the cannabinoid acids and terpenes are well understood, studies regarding their elicitation have been largely inconclusive. Through exogenous application, an array of phytohormones have been linked to increased or altered cannabinoid and terpene production, specifically stress hormone abscisic acid (ABA) and flowering hormone jasmonic acid (JA). Due to limitations in exogenous hormone application, however, the effects of short-lived phytohormone nitric oxide (NO) have not been assessed, despite implications of its involvement in the elicitation of cannabinoid production. In this study, Cannabis sativa plants of a high Δ9-tetrahydrocannabinol (THC) cultivar were inoculated with Azospirillum brasilense, a plant-growth promoting rhizobacteria capable of producing NO. Plants were grown under standard conditions in a coconut fibre medium for 60 days. At this time, floral inflorescence were harvested and total terpene and cannabinoid content was analysed using Gas Chromatography Mass Spectrometry and High Performance Liquid Chromatography, respectively. In addition, soil samples were taken prior to inoculation and at harvest. From these samples, total microbial DNA was extracted and analyzed using next generation sequencing of the conserved 16S region. This poster will display the effects of A. brasilense on Cannabis terpene and cannabinoid content, as well as its effect on microbial community composition and density. We hope to emphasis with these results the importance of biofertilization in this emerging agricultural field
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".