Improved Methods for Treatment of Phytopathogenic Biofilms: Metallic Compounds as Anti-Bacterial Coatings and Fungicide Tank-Mix Partners
Bibliographic record
Abstract
Fungi and bacteria cause disease issues in cultivated plants world-wide. In most cases, the fungi and bacteria colonize plant tissues as biofilms, which can be very challenging to destroy or eradicate. In this experiment, we employed a novel (biofilm) approach to crop disease management by evaluating the efficacies of six fungicides, and four silver-based compounds, versus biofilms formed by fungi and bacteria, respectively. The aim was to identify combinations of fungicides and metallic cations that showed potential to improve the control of white mold (WM), caused by the ascomycete fungus Sclerotinia sclerotiorum, and to evaluate novel high valency silver compounds as seed coatings to prevent biofilm formation of four bacterial blight pathogens on dry bean seeds. Our results confirmed that mature fungal biofilms were recalcitrant to inactivation by fungicides. When metallic cations were added to the fungicides, their efficacies were improved. Some improvements were statistically significant, with one combination (fluazinam + Cu2+) showing a synergistic effect. Additionally, coatings with silver compounds could reduce bacterial blight biofilms on dry bean seeds and oxysilver nitrate was the most potent inhibitor of bacterial blight.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".