The Significance of Fungal Biofilms in Association with Plants and Soils
Bibliographic record
Abstract
Microorganisms such as fungi, bacteria, viruses, and protists cause diseases on food, feed, and fiber crops every year. Diseases result in significant economic losses to producers and processors, and, in some cases, may present a public health risk to consumers. As the body of knowledge regarding microbial biology increases, this knowledge helps improve disease avoidance, management, and control. For example, the more one knows about the conditions and mechanisms controlling microorganisms' growth, infection, survival, and dispersal, the better able one is to design effective strategies to prevent disease, manage infections, and control the negative impacts. The discovery, description, and characterization of microbial biofilms is a recent example of how understanding pathogen biology to a greater extent can help in management of disease. The documented knowledge of microbial biofilms has led to a shift in our understanding of how microorganisms grow, survive, adapt, and exploit hosts. We now understand a great deal about biofilms formed by a number of bacterial and yeast species in aquatic and clinical settings. However, much less is known about biofilms associated with plants, especially those formed by filamentous fungi. This chapter reviews what is known about biofilms that have been characterized on plants or in soil, with special attention to those of filamentous fungi on plants, including mycorrhizae, as well as Oomycetes. These examples will help drive plant disease management toward a biofilm approach that is based on a greater understanding of how the causal agents grow, invade, survive, and disperse. This approach may strengthen existing programs aimed at improving soil and plant health.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".