BIOFUMIGATION: PROSPECTS FOR CONTROL OF SOIL BORNE PLANT DISEASES
Bibliographic record
Abstract
One of the biggest worries for farmers is the spread of pathogens through the soil. These diseases are difficult to control because they are often tiny in size, buried in the soil, and frequently highly harmful even in small numbers. The Montreal Protocol on Substances that Deplete the Ozone Layer, to which the majority of countries are signatories, has restricted the use of residual pesticides for the management of soil-borne infections, and the demand for food that is free of blemishes is rising. However, it has become urgently necessary to find suitable substitutes as a result of the phase-out of methyl bromide, a significant chemical. After introducing plants that contain glucosinolate, which is digested to produce isothiocyanates (ITC) in the soil, biofumigation has emerged as a crucial procedure to control plant diseases. The existence of glucosinolates and the byproducts of their hydrolysis in soil illustrate the effectiveness and environmental impact of biofumigation. The most significant producers of bioactive chemicals are Brassica species, which makes them suitable for biofumigation applications. This review focuses on the concept, the effective application of biofumigants against soil-borne diseases, and offers several case examples to highlight upcoming difficulties for the concept's continued advancement.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".