Photodynamic Inactivation of microorganisms for agricultural applications
Bibliographic record
Abstract
Plant pathogens cause severe harvest losses and bacterial food contaminations kill people worldwide. Photodynamic Inactivation (PDI) might be applicable to control pathogens in agriculture. We here test PDI based on Sodium Magnesium Chlorophyllin and LED light or sunlight activation against relevant bacterial and fungal plant pathogens as well as Listeria on seeds and sprouts. Photodynamic Inactivation is highly effective against Gram+ and Gram- bacterial plant pathogens in suspension and in situ on leaves with a reduction of microbial count for up to seven log steps. Seeds can be effectively decontaminated from Listeria innocua by PDI, and the effect is propagated to sprouts. If based on natural photosensitizers such as Sodium Magnesium Chlorophyllin, PDI, might allow for effective, ecofriendly and economic control of plant pathogens and microbial contaminations of seeds as food.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".