Dose-responses of Bacillus cereus RS87 for growth enhancement in various Thai rice cultivars
Bibliographic record
Abstract
To achieve the goal of reducing the levels of chemical fertilizers applied in rice production, there is a need to develop microorganisms with the capacity to enhance plant growth. Previous studies have demonstrated that Bacillus cereus RS87 promotes growth of various plants in greenhouse and field trials. The objectives of this study were to (i) evaluate the efficacy and determine the optimum concentration of B. cereus RS87 to enhance growth of various Thai rice cultivars, (ii) measure the chlorophyll content in leaves affected by strain RS87, and (iii) investigate the capacity of strain RS87 to solubilize phosphate and produce siderophores. Three concentrations of strain RS87 (log 6.0, log 7.0, and log 8.0 CFU/mL) were applied to each rice cultivar. Superior responses (i.e., enhanced development of roots and shoots of all rice cultivars) were observed using RS87 at log 8.0 CFU/mL compared with lower bacterial concentrations and the water-treated control treatment. In addition, log 8.0 CFU/mL of RS87 provided the greatest root length and plant height of all rice cultivars 45 days after planting in the greenhouse. Rice leaves treated with log 8.0 CFU/mL of RS87 yielded the highest total chlorophyll, specifically chlorophyll a and chlorophyll b, compared with the control. Strain RS87 also solubilized phosphate and produced siderophores. The results of these studies demonstrate that log 8.0 CFU/mL is the optimum concentration of strain RS87 for growth promotion of various Thai rice cultivars.
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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 | 0.001 |
| 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".