Biocontrol of sclerotinia stem rot ( <i>Sclerotinia sclerotiorum</i> ) of soybean using novel <i>Bacillus subtilis</i> strain SB24 under control conditions
Bibliographic record
Abstract
Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum , is a major disease of soybean in Canada. Laboratory and greenhouse experiments were conducted to evaluate potential effectiveness of cell suspensions, cell‐free culture filtrates and broth cultures of Bacillus subtilis strain SB24 for suppression of SSR. The SB24 cell suspensions and cell‐free culture filtrates significantly reduced mycelial growth of S. sclerotiorum by 50 to 75% and suppressed sclerotial formation by > 90%. The severity on soybean was negatively correlated ( r < −0·84, P < 0·01) to the concentrations of cell suspension, cell‐free culture filtrate and broth culture applied. The cell suspension and broth culture preparations significantly ( P < 0·01) reduced SSR severity by 45 to 90% at concentrations ranging from 5 × 10 6 to 10 9 CFU mL −1 . The most effective concentration was 5 × 10 8 CFU mL −1 for all three preparations, reducing the severity by 60 to 90%. The B. subtilis SB24 was most effective in reducing disease severity when applied ≤ 24 h before plant inoculation with S. sclerotiorum and a significant effectiveness was observed up to 15 days after plant inoculation. The population density of B. subtilis on soybean leaves decreased by 1·5 to 2·5 log units over 15 days under field conditions, and by 0·8 log units over 5 weeks under control conditions. The decrease in population density was significantly correlated with rainfall in the field ( r < −0·93, P < 0·01), suggesting that the biocontrol bacteria may be washed away by rain.
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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 | 0.000 |
| 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".