Mycoviruses in Biological Control: From Basic Research to Field Implementation
Bibliographic record
Abstract
Mycoviruses from plant pathogens can induce hypovirulence (reduced virulence) in their host fungi and have gained considerable attention as potential biocontrol tools. An increasing number of mycoviruses that induce fungal hypovirulence, from a wide variety of taxonomic groups, are currently being reported. Successful application of these viruses in disease management is greatly dependent on their ability to spread in the natural populations of the pathogen. Mycoviruses generally lack extracellular routes of transmission. Hyphal anastomosis is the main route of horizontal mycovirus transmission to other isolates, and conidia of vertical transmission to the progeny. Transmission efficiencies are influenced by both the fungal host and the infecting virus. Interestingly, artificial transfection methods have shown that potential biocontrol mycoviruses often have the ability to infect a variety of fungi. This expands their possible use to the control of pathogens others than those where they were identified. Mycovirus research is also focused on gaining insights into their complex molecular biology and the molecular bases of fungus-virus interactions. This knowledge could be exploited to manipulate the mycovirus and/or the host and generate combinations with enhanced properties in biological control. Finally, when exploring the use of mycoviruses in field conditions, the pathogen life style and the characteristics of the disease and crops affected will deeply impact the specific challenges to overcome, and the development of biocontrol formulations and delivery methods.
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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 | 0.018 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".