Management of powdery mildew, caused by <i>Erysiphe cruciferarum</i>, on wasabi (<i>Wasabia japonica</i>) plants in British Columbia
Bibliographic record
Abstract
Powdery mildew on wasabi (Wasabia japonica (Miq.) Matsumura)plants reduces photosynthesis and severe infections can result in chlorosis and defoliation, resulting in reduced yields. Erysiphe cruciferarum Opiz ex L. Junell was confirmed as the causal agent using sequence analysis of the ITS1-5.8 S-ITS2 region, along with conidial and conidiophore morphology. To evaluate reduced-risk management options, the efficacy of four commercially available products was assessed over three separate trials conducted in a commercial wasabi greenhouse in British Columbia. The products tested included two biological pesticides, Bacillus subtilis strain QST 713 (formulated as Rhapsody ASO), and Streptomyces lydicus strain WYEC 108 (formulated as Actinovate SP), as well as a copper-based fungicide, Cueva, and a plant formulated extract, Regalia Maxx (from Reynoutria sachalinensis). Treatments were applied at two-week intervals over 10–12 weeks starting with onset of disease symptoms. Natural infection by E. cruciferarum was allowed to progress on the plants during the trials. Leaves were assessed for powdery mildew biweekly based on percentage of leaf surface infected, which was then converted to area under the disease progress curve (AUDPC) for each treatment. Five to six applications of Cueva and Regalia significantly (p
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".