Understanding bud rot development, caused by <i>Botrytis cinerea</i>, on cannabis (<i>Cannabis sativa</i> L.) plants grown under greenhouse conditions
Bibliographic record
Abstract
Botrytis cinerea is a widespread necrotrophic plant pathogen that causes diseases on >1000 plant species, including vegetables and ornamental greenhouse crops. On cannabis ( Cannabis sativ a L.), the pathogen is responsible for causing “bud rot”, a major disease affecting the inflorescences (compound flowers), as well as seedling damping-off and leaf blight under certain conditions. During greenhouse cultivation, Botrytis cinerea can destroy cannabis inflorescences rapidly under optimal relative humidity conditions (>70%) and moderate temperatures (17–24 °C). Little is currently known about the host–pathogen interactions of Botrytis cinerea on cannabis. Information gleaned from other hosts can provide valuable insights for comparative purposes to understand disease development, epidemiology, and pathogenicity of Botrytis cinerea on cannabis crops. This review describes the pathogenesis and host responses to Botrytis infection and assesses potential mechanisms involved in disease resistance. The effects of microclimatic and other environmental conditions on disease development, strategies for early disease detection using prediction models, and the application of biological control agents that can prevent Botrytis cinerea development on cannabis are discussed. Other potential disease management approaches to reduce the impact of Botrytis bud rot are also reviewed. Numerous opportunities for conducting additional research to better understand the cannabis– Botrytis cinerea interaction are identified.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".