Evaluation of bacteria isolated from rice for plant growth promotion and biological control of seedling disease of rice
Bibliographic record
Abstract
Of 102 rhizoplane and endophytic bacteria isolated from rice roots and stems in California, 37% significantly (P < or = 0.05) inhibited the growth in vitro of two pathogens, Achlya klebsiana and Pythium spinosum, causing seedling disease of rice. Four endophytic strains were highly effective against seedling disease in growth pouch assays, and these were identified as Pseudomonas fluorescens (S3), Pseudomonas tolaasii (S20), Pseudomonas veronii (S21), and Sphingomonas trueperi (S12) by sequencing of amplified 16S rRNA genes. Strains S12, S20, and S21 contained the nitrogen fixation gene, nifD, but only S12 was able to reduce acetylene in pure culture. The four strains significantly enhanced plant growth in the absence of pathogens, as evidenced by increases in plant height and dry weight of inoculated rice seedlings relative to noninoculated rice. Three bacterial strains (S3, S20, and S21) were evaluated in pot bioassays and reduced disease incidence by 50%-73%. Strain S3 was as effective at suppressing disease at the lowest inoculum density (106 CFU/mL) as at higher density (10(8) CFU/mL or undiluted suspension). This study indicates that selected endophytic bacterial strains have potential for control of seedling disease of rice and for plant growth promotion.
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) | 0.001 | 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 | 0.000 |
| 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".