Enhancing biological control efficacy of yeasts to control fungal diseases through biotechnology.
Bibliographic record
Abstract
Classical biological control of fungal plant diseases by yeasts and yeast-like organisms has been amply described in the literature, yet few commercial products have arisen out of those research efforts. It is generally agreed that a better understanding of: (i) the ecological conditions affecting the activity of a biocontrol agent (BCA); and (ii) the mode(s) of action conferring to a BCA its properties will greatly help improve its efficacy. In this context, the recent advances in biotechnology are providing scientists with new tools to study and improve BCAs. Coincidently, yeasts and yeast-like fungi are particularly amenable to genetic transformation and thus biotechnology techniques. While improving the efficacy of BCAs through techniques affecting gene expression is a promising endeavour, other technologies such as Green Fluorescent Protein (GFP) expression have allowed precise ecological studies of plant-pathogen interactions, and recently, that of BCAs with the host plant and fungal pathogen. These studies can be extremely useful to elucidate the mode of action and determine the ecological fate of the BCAs, as in the case of Pseudozyma flocculosa, the registered yeast-like BCA of powdery mildews. Other tools such as reverse genetics approaches based on data from genomic sequencing using RNA interference (RNAi) or gene knockout have the potential to identify key genes involved in biocontrol activity and thus help achieve the improvement of biocontrol efficacy of yeasts through biotechnology.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".