Bacillus-Based Products to Control Meloidogyne incognita Races 3 and 4 in Cotton and Compared Histopathology Using B. methylotrophicus
Bibliographic record
Abstract
Cotton meloidoginosis caused by Meloidogyne incognita races 3 and 4 is an important disease and can cause intense damage. The objective of this work was to test the effectiveness of commercial Bacillus-based products in the control of M. incognita races 3 and 4 of cotton in a greenhouse. Plants with and without nematode inoculation were used and subsequently evaluated for 200 days after the application of treatments. The products with Bacillus subtilis, B. amyloliquefasciens and B. subtilis + B. lincheniformis showed the best results in terms of shoot fresh matter weight. In relation to shoot dry weight, treatments with B. methylotrophicus (87 g, with nematode) and B. subtilis (80 g, with nematode) were superior, with emphasis on B. methylotrophicus. In the evaluation of root fresh weight, only the treatment with B. methylotrophicus (148.8 g, with nematode) provided statistically higher weight than the control. In item dry weight of root without nematode and nematode reproduction factor, the treatment with B. methylotrophicus stood out from the other treatments, making this the selected product to conduct the subsequent tests. With the acid fuchsin staining method, it was possible to verify that there was reduction in the penetration of J2 in the first days for plants treated with the bacteria. Upon adoption of the toluidine blue staining method, it was possible to observe abnormalities in giant cells with formation of vacuoles, thinner cell wall and females with large vacuoles inside. Thus, there is evidence that the use of biological products can be effective in controlling M. incognita.
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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".