Plant Growth-Promoting Rhizobacteria (PGPR) with Microbial Growth Broth Improve Biomass and Secondary Metabolite Accumulation of <i>Cannabis sativa</i> L.
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Plant growth-promoting rhizobacteria (PGPR) are a sustainable crop production input; some show positive effects under laboratory conditions but poorly colonize host field-grown plants. Inoculating with PGPR in microbial growth medium ( e.g., King’s B) could overcome this. We evaluated cannabis plant (cv. CBD Kush) growth promotion by inoculating three PGPR ( Bacillus sp., Mucilaginibacter sp., and Pseudomonas sp.) in King’s B at vegetative and flower stages. At the vegetative stage, Mucilaginibacter sp. inoculation increased flower dry weight (24%), total CBD (11.1%), and THC (11.6%); Pseudomonas sp. increased stem (28%) dry matter, total CBD (7.2%), and THC (5.9%); and Bacillus sp. increased total THC by 4.8%. Inoculation with Mucilaginibacter sp. and Pseudomonas sp. at the flowering stage led to 23 and 18% increases in total terpene accumulation, respectively. Overall, vegetative inoculation with PGPR enhanced cannabis yield attributes and chemical profiles. Further research into PGPR inoculation onto cannabis and the subsequent level of colonization could provide key insights regarding PGPR-host interactions.
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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) | 0.001 | 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".