Rain-induced Ejection of Pathogens from Leaves: Revisiting the Hypothesis of Splash-on-Film using High-speed Visualization
Bibliographic record
Abstract
Plant diseases are a major cause of losses of crops worldwide. Although rainfalls and foliar disease outbreaks are correlated, the detailed mechanism explaining their link remains poorly understood. The common assumption from phytopathology for such link is that a splash is generated upon impact of raindrops on contaminated liquid films coating sick leaves. We examine this assumption using direct high-speed visualizations of the interactions of raindrops and leaves over a range of plants. We show that films are seldom found on the surface of common leaves. We quantify the leaf-surface's wetting properties, showing that sessile droplets instead of films are predominant on the surfaces of leaves. We find that the presence of sessile drops rather than that of films has important implications when coupled with the compliance of a leaf: it leads to a new physical picture consisting of two dominant rain-induced mechanisms of ejection of pathogens. The first involves a direct interaction between the fluids of the raindrop and the sessile drops via an off-centered splash. The second involves the indirect action of the raindrop that leads to the inertial detachment of the sessile drop via the leaf's motion imparted by the impact of the raindrop. Both mechanisms are distinct from the commonly assumed scenario of splash-on-film in terms of outcome: they result in different fragmentation processes induced by surface tension, and, thus, different size-distributions of droplets ejected. This is the first time that modern direct high-speed visualizations of impacts on leaves are used to examine rain-induced ejection of pathogens at the level of a leaf and identify the inertial detachment and off-center splash ejections as alternatives to the classically assumed splash-on-film ejections of foliar pathogens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".