Successful strategy for the selection of new strawberry-associated rhizobacteria antagonistic to Verticillium wilt
Bibliographic record
Abstract
In order to isolate and characterize new strawberry-associated bacteria antagonistic to the soil-borne pathogenic fungus Verticillium dahliae Kleb., rhizobacterial populations from two different strawberry species, Greenish Strawberry (Fragaria viridis) and Garden Strawberry (F. x ananassa) obtained after plating onto King's B and glycerol-arginine agar, were screened for in vitro antagonism toward V. dahliae. The proportion of isolates with antifungal activity determined in in vitro assay against V. dahliae was higher for the Garden Strawberry than for the Greenish Strawberry. From 300 isolates, 20 isolates with strong antifungal activity were selected characterized by physiological profiling and molecular fingerprinting methods. Diversity among the isolates was characterized with molecular fingerprints using amplified ribosomal DNA restriction analysis (ARDRA) and the more discriminating BOX-PCR fingerprint method. The physiological profiles were well correlated with molecular fingerprinting pattern analysis. Significant reduction of Verticillium wilt by bacterial dipping bath treatment was shown in the greenhouse and in fields naturally infested by V. dahliae. The relative increase of yield ranged from 117% (Streptomyces albidoflavus S1) to 344% (Pseudomonas fluorescens P10) in greenhouse trials, and 113% (Streptomyces albidoflavus S1) to 247% (Pseudomonas fluorescens P6) in field trials. Evaluation resulted in the selection of three effective biocontrol agents (Pseudomonas fluorescens P6, P10, and Streptomyces diastatochromogenes S9) antagonistic to the Verticillium wilt pathogen.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".