<i>Botrytis cinerea</i>management in ornamental production: a continuous battle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ornamental production systems are complicated to manage due to the many species and genera that may be grown and handled together on a single production site. Ornamentals are threatened by various phytopathogenic fungi in greenhouse and field production. Among these, Botrytis cinerea is one of the most notorious pathogens of ornamentals, specifically cut flowers. B. cinerea is responsible for causing Botrytis blight disease in both pre- and post-harvest conditions. The pathogen infects leaves, stems, flowers, etc., and causes petal specking, flower blight, sepal yellowing, and peduncle bending, among other symptoms. The ability of B. cinerea to cause disease in greenhouses and fields, as well as in subsequent handling, storage, and transportation, makes this fungus an important pathogen due to its potential negative economic effects on the cut flower industry. For the management of B. cinerea, the routine application of fungicides is considered a major tool in commercial production. However, fungicide resistance, phytotoxicity, application residues, environmental concerns, and health issues have forced growers to seek alternative management approaches. In this review paper, we discuss the different approaches (classic to novel strategies) used for B. cinerea management, including chemical methods and their modes of action. The integration of new practices with existing management strategies (sanitation, nutrition, plant regulators, botanical extracts, biological control, fungicides) could provide effective results in ornamental production systems. Understanding the ecology of pathosystems, disease epidemiology and the integration of all possible management measures as a system approach may also provide adequate disease suppression in both pre- and post-harvest conditions.
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.
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 | 0.000 | 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) | 0.000 | 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 it