Combining Desirable Traits for a Good Biocontrol Strategy against Sclerotinia sclerotiorum
Bibliographic record
Abstract
The fungal pathogen Sclerotinia sclerotiorum (Helotiales: Sclerotiniaceae) causes white mold, a disease that leads to substantial losses on a wide variety of hosts throughout the world. This economically important fungus affects yield and seed quality, and its control mostly relies on the use of environmentally damaging fungicides. This review aimed to present the latest discoveries on microorganisms and the biocontrol mechanisms used against white mold. A special focus is put on the identification of biocontrol desirable traits required for efficient disease control. A better understanding of the mechanisms involved and the conditions required for their action is also essential to ensure a successful implementation of biocontrol under commercial field conditions. In this review, a brief introduction on the pathogen, its disease cycle, and its main pathogenicity factors is presented, followed by a thorough description of the microorganisms that have so far demonstrated biocontrol potential against white mold and the mechanisms they use to achieve control. Antibiosis, induced systemic resistance, mycoparasitism, and hypovirulence are discussed. Finally, based on our actual knowledge, the best control strategies against S. sclerotiorum that are likely to succeed commercially are discussed, including combining biocontrol desirable traits of particular interest.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".