Solubilization of potassium-bearing minerals by a wild-type strain of <i>Bacillus edaphicus</i> and its mutants and increased potassium uptake by wheat
Bibliographic record
Abstract
Two potassium (K)-bearing minerals, Nanjing feldspar and Suzhou illite, were used to investigate K mobilization by the wild-type strain NBT of Bacillus edaphicus, also labeled MPs+, selected for high activity in mobilizing potassium from minerals, and by four of its UV + LiCl mutants, MPs++, MPs+1, MPs+2, and MPs-. In liquid cultures, the five bacterial strains showed better growth on Suzhou illite than on Nanjing feldspar. Suzhou illite was the better potassium source for the growth of the wild type and the MPs++, MPs+1, and MPs+2 mutants. Solubilization of K from its sources by the wild-type NBT and the MPs++ mutant resulted mostly from the action of organic acids and capsular polysaccharides. Oxalic acid seemed to be a more active agent for the solubilization of Nanjing feldspar. Oxalic and tartaric acids were likely involved in the solubilization of Suzhou illite. The MPs- mutant did not produce any organic acid or capsular polysaccharide when grown on the above two K sources. In a pot experiment, wheat (Triticum aestivum L.) 'Yangmai-158' was grown in a yellow-brown soil that had low available K. After inoculation with bacterial strains, B. edaphicus NBT and its four mutants, MPs++, MPs+1, MPs+2, and MPs- (in separate tests), the root growth and shoot growth of wheat were significantly increased by B. edaphicus NBT and the mutants MPs++ and MPs+1. Bacterial inoculation also resulted in significantly higher N, P, and K contents of plant components. The bacteria were able to survive in the wheat rhizosphere soils after root inoculation.
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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) | 0.000 | 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".