Management of the Common Bacterial Blight of the Bean by Rhodotorula glutinis and Sporidiobolus johnsonii
Bibliographic record
Abstract
Bean common bacterial blight reduces crop productivity and is difficult to control. However, biological control by yeast can be an efficient complementary measure in management. The objective was to evaluate the ability of Rhodotorula glutinis and Sporidiobolus johnsonii to reduce the severity of bean common bacterial blight. The cultivar used was IAPAR Tuiuiú. The first experiment was sown in March and repeated in October, in a 4 × 3 factorial scheme (zero, one, two and three applications and three treatments R. glutinis, S. johnsonii and Acibenzolar-S-Methyl (ASM)). For this purpose were evaluated the area under the disease progress curve (AACPD), number of pods per plant (NVP), number of grains per pod (NGV), thousand grain mass (MMG) and productivity. For the results of the March cultivation, due to the low temperature, the maximum severity of bean common bacterial blight was 8% and the applications of yeasts were not significant for AACPD. The isolate R. glutinis showed the highest average of productivity with two applications, being 1006.44 kg ha-1. For October cultivation, R. glutinis and S. johnsonii isolates reduced AACPD by 66.84 and 58.42%, respectively with three applications. For productivity, R. glutinis and S. johnsonii showed no difference between the number of applications. The ASM showed a productivity of 4418.56 kg ha-1 with three applications. The results indicate that the yeasts R. glutinis and S. johnsonii reduce the severity of bean common bacterial blight and the most appropriate number of applications are two for both isolates.
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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 | 0.000 |
| 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".