Effects of inoculation with organic-phosphorus-mineralizing bacteria on soybean (<i>Glycine max</i>) growth and indigenous bacterial community diversity
Bibliographic record
Abstract
Three different organic-phosphorus-mineralizing bacteria (OPMB) strains were inoculated to soil planted with soybean (Glycine max), and their effects on soybean growth and indigenous bacterial community diversity were investigated. Inoculation with Pseudomonas fluorescens Z4-1 and Brevibacillus agri L7-1 increased organic phosphorus degradation by 22% and 30%, respectively, compared with the control at the mature stage. Strains P. fluorescens Z4-1 and B. agri L7-1 significantly improved the soil alkaline phosphatase activity, average well color development, and the soybean root activity. Terminal restriction fragment length polymorphism analysis demonstrated that P. fluorescens Z4-1 and B. agri L7-1 could persist in the soil at relative abundances of 2.0%-6.4% throughout soybean growth. Thus, P. fluorescens Z4-1 and B. agri L7-1 could potentially be used in organic-phosphorus-mineralizing biofertilizers. OPMB inoculation altered the genetic structure of the soil bacterial communities but had no apparent influence on the carbon source utilization profiles of the soil bacterial communities. Principal components analysis showed that the changes in the carbon source utilization profiles of bacterial community depended mainly on the plant growth stages rather than inoculation with OPMB. The results help to understand the evolution of the soil bacterial community after OPMB inoculation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".