Sub-optimal nutrient regime coupled with Bacillus and Pseudomonas sp. inoculation influences trichome density and cannabinoid profiles in drug-type Cannabis sativa
Bibliographic record
Abstract
Cannabis sativa remains under heavy legal restriction around the globe that prevents extensive investigations into agricultural applications for improving its development. This work investigates the potential of specific plant growth-promoting rhizobacteria (PGPR) to improve Cannabis cannabinoid yield through increased trichome densities on floral organs, and to determine if sub-optimal environmental conditions would affect the outcomes of PGPR presence by altering plant development and cannabinoid profiles. Here, Pseudomonas sp. or Bacillus sp. were applied to the root system either separately or in a consortium to determine the effect of this bacterial treatment on the density of stalked glandular trichomes. Further, a low nutrient regime was applied for the first half of plant development to determine if an environmental stressor interacts with the effects of the microbial treatments on stalked trichome densities. Following 8 weeks of flower development, trichome density on calyces and bracts of inflorescences were determined using microscopy. Our findings unexpectedly indicate that recommended nutrient levels were linked to a decreasing trend in trichome densities with PGPR inoculations, but a low nutrient regime coupled with PGPR treatment increased them. Cannabinoid content is partially consistent with these results, in that a low nutrient regime increased the abundance of key cannabinoids compared to recommended regimes, with Bacillus sp. inoculation linked to the greatest number of significant changes between the two nutrient regimes. Overall, this work provides insight into how PGPR presence affects Cannabis stalked trichome development and cannabinoid profiles, and how environmental stressors can affect, and even enhance, trichome densities and influence major cannabinoid production, thereby pointing towards avenues for reducing the reliance on synthetic fertilizers during plant production without compromising yield.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".