Baseline and Differential Sensitivity to Two QoI Fungicides Among Isolates of <i>Phytophthora cactorum</i> That Cause Leather Rot and Crown Rot on Strawberry
Bibliographic record
Abstract
Sensitivities of 89 isolates of Phytophthora cactorum, the causal agent of crown rot and leather rot on strawberry plants, from seven states (Florida, Maine, North Carolina, Ohio, Oregon, South Carolina, and New York) to the QoI fungicide azoxystrobin were determined based on mycelium growth and zoospore germination. Radial growth of mycelia on lima bean agar amended with azoxystrobin at 0.001, 0.01, 0.1, 1.0, 10, and 30 μg/ml and salicylhydroxamic acid (SHAM) at 100 μg/ml was measured after 6 days. Effect on zoospore germination was evaluated in aqueous solutions of azoxystrobin at 0.005, 0.01, 0.05, 0.1, 0.5, and 1.0 μg/ml in 96-well microtiter plates by counting germinated and nongerminated zoospores after 4 h at room temperature. SHAM was not used to evaluate zoospore sensitivity. The effective dose to reduce mycelium growth by 50% (ED50) ranged from 0.16 to 12.52 μg/ml for leather rot isolates and 0.10 to 15 μg/ml for crown rot isolates. The Kolmogorov-Smirnov test showed significant differences (P < 0.001) between the two distributions. Zoospores were much more sensitive to azoxystrobin than were mycelia. Differences between sensitivity distributions for zoospores from leather rot and crown rot isolates were significant at P = 0.05. Estimated ED50 values ranged from 0.01 to 0.24 μg/ml with a median of 0.04 μg/ml. Experiments with pyraclostrobin, another QoI fungicide, demonstrated that both mycelia and zoospores of P. cactorum were more sensitive to pyraclostrobin than to azoxystrobin. Sensitivities to azoxystrobin and pyraclostrobin were moderately but significantly correlated (r = 0.60, P = 0.0001).
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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 | 0.000 | 0.000 |
| 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".