Effects of biological and chemical treatments on<i>Botrytis</i>stem canker and fruit yield of tomato under greenhouse conditions<sup>1</sup>
Bibliographic record
Abstract
Experiments were conducted to identify, by in vitro dual culture tests, potential biological control agents producing antibiotics and to evaluate selected biological and chemical agents for control of stem canker caused by Botrytis cinerea on tomato plants (Lycopersicon esculentum) grown in yellow cedar sawdust in a research greenhouse. Four strains of Bacillus subtilis and one each of Enterobacter agglomerans and Rhodosporidium diobovatum were antagonistic towards B. cinerea in dual culture. Lesions in treatments with RootShield® and a strain of R. diobovatum were significantly shorter compared with the inoculated control. Plants treated with RootShield® or R. diobovatum had significantly higher total fruit yield than the inoculated control. The treatments with RootShield®, SoilGard®, and R. diobovatum produced significantly more total fruits than the inoculated control. The number of dead plants were significantly lower in treatments with RootShield® and R. diobovatum compared with the other treatments and inoculated control. These results suggest that RootShield® and R. diobovatum have the potential to control Botrytis stem canker and increase the fruit yield of tomato under greenhouse conditions.Key words: biological control, Botrytis cinerea, Bacillus subtilis, Enterobacter agglomerans, Rhodosporidium diobovatum, gray mold.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".