Suppression of<i>Monilinia</i>Blight: Strategies for Today and Potential Fungicide Options for Tomorrow
Bibliographic record
Abstract
Fungicide efficacy field trials were conducted in Nova Scotia and Prince Edward Island during the 2009 growing season to address concerns over: (i) the possible tolerance of the fungal pathogen [Monilinia vaccinii-corymbosi (Reade) Honey] causing Monilinia blight or mummy berry to presently registered fungicides; (ii) increasing scrutiny of specific fungicides by end users; and (iii) the challenge of trying to apply a curative fungicide in a timely manner after an infection period has likely occurred. Results indicated that the active ingredients propiconazole (Topas® and Orbit®), prothioconazole (Proline®), and penthiopyrad (LEM17) were very effective in suppressing Monilinia blight, tolerance to propiconazole did not appear to be present, and the use of multiple active ingredients with different modes of action and persistence also provided excellent suppression. In addition, the treatments had a profound influence on berry yield with the propiconazole, prothioconazole, and penthiopyrad treatments having berry yields that were 73.6, 111, and 112% greater than the untreated control, respectively, at the Nova Scotia site. However, these berry yield results need to be viewed with caution given the known suppressive attributes of some of these fungicides to other diseases present during Monilinia infection periods.
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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 | 0.000 | 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) | 0.002 | 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".