Bacillus and Paenibacillus as plant growth-promoting bacteria in soybean and cannabis
Bibliographic record
Abstract
Many agrochemicals designed to help plants withstand abiotic and biotic stresses can negatively impact the environment. Soil, as an essential natural resource, offers plants organic matter, nutrients, and microbial diversity to thrive in challenging environmental conditions. The soil contains plant growth-promoting bacteria that play an important role in plant/crop productivity, assisting plants through a variety of mechanisms, including nitrogen fixation, phosphate solubilization, phytohormone production, induction of resistance against a wide range of pathogens, and production of microbe-to-plant signals that regulate aspects of plant responses to stress. Plant growth-promoting bacteria such as Bacillus and Paenibacillus can contribute to sustainable agriculture by enhancing nutrient uptake, acting as biocontrol agents, and producing lytic enzymes with the potential to disrupt or destroy pathogenic organisms in important agricultural and medicinal crops including soybean and cannabis. This review explores the mechanisms of action of plant growth-promoting bacteria, focusing on Bacillus and Paenibacillus species, and their potential to enhance, optimize plant growth and promote sustainable agriculture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".