Evaluation of disease management approaches for powdery mildew on<i>Cannabis sativa</i>L. (marijuana) plants
Bibliographic record
Abstract
Powdery mildew on cannabis (Cannabis sativa L., marijuana), caused by Golovinomyces cichoracearum, reduces plant growth and overall quality. To investigate disease management options, biological, chemical and physical approaches were assessed. A mildew-susceptible strain, ‘Copenhagen Kush’, was grown indoors with continual exposure to mildew inoculum. Treatments were applied weekly over a four-week period to groups of four plants once mildew infection had established itself. Trials were repeated thrice under varying initial disease pressures. Disease assessments were made weekly and the percentage of area infected on 30 leaflets per plant was used to calculate a disease rating score for treated and control plants. Disease progress curves were plotted and AUDPC values were determined for each treatment. To test the effect of UV-C light on mildew development, plants were exposed daily for 3–5 s over 28 days to UV-C light. The response of 12 cannabis strains to powdery mildew infection was assessed after exposing them to inoculum over a period of two weeks. The most effective treatments that significantly (P < 0.05) reduced disease in three trials were Luna Privilege SC (fluopyram), Regalia® Maxx, MilStop®, Rhapsody ASOTM, neem oil, and Stargus®. Treatments that were less effective included ZeroTol®, boric acid, and Actinovate® SP. Daily exposure of plants to UV-C light significantly reduced disease (by 45.2%, P < 0.05). Seven of 12 cannabis strains had significantly lower disease severity compared with the other five strains. The disease management strategies evaluated in this study have potential for reducing powdery mildew development on cannabis.
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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 | 0.000 | 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 | 0.001 |
| 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".