Overview of methods and considerations for the photodynamic inactivation of microorganisms for agricultural applications
Bibliographic record
Abstract
Antimicrobial resistance in agriculture is a global concern and carries huge financial consequences. Despite that, practical solutions for growers that are sustainable, low cost and environmentally friendly have been sparse. This has created opportunities for the agrochemical industry to develop pesticides with novel modes of action. Recently the use of photodynamic inactivation (PDI), classically used in cancer treatments, has been explored in agriculture as an alternative to traditional chemistries, mainly as a promising new approach for the eradication of pesticide resistant strains. However, applications in the field pose unique challenges and call for new methods of evaluation to adequately address issues specific to PDI applications in plants and challenges faced in the field. The aim of this review is to summarize in vitro, ex vivo, and in vivo/in planta experimental strategies and methods used to test and evaluate photodynamic agents as photo-responsive pesticides for applications in agriculture. The review highlights some of the strategies that have been explored to overcome challenges in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".