Evaluation of Northern Grape Hybrid Cultivars for Their Susceptibility to Anthracnose Caused by <i>Elsinoe ampelina</i>
Bibliographic record
Abstract
Use of winter-hardy grape cultivars has enabled expansion of wine grape production in the province of Quebec, Canada, but some of the cultivars have been reported as susceptible to grape anthracnose, a serious disease. The disease causes grape leaves and berries to dry up and drop prematurely, resulting in poor or no yield and, in cold climates, reduced winter survival. On susceptible cultivars, anthracnose management is costly, making grape production unprofitable. Therefore, cold climate grape cultivars were evaluated for their susceptibility to leaf infection by E. ampelina. Plants were grown from dormant cuttings and on each plant the youngest three leaves were tagged and inoculated with a conidial suspension of E. ampelina (1 × 106 spores per ml). Immediately after inoculation, plants were maintained under high humidity conditions at 24 ± 2°C for 72 h. The number of lesions per leaf was determined 14 days after inoculation. Cluster analysis was used to group the cultivars based on their susceptibility. The cultivars were classified as: (i) resistant or slightly susceptible – DM 8521-1, ES 10-18-30, St-Pépin, Sabrevois, Vidal banc, Baltica, Frontenac gris, Ste-Croix, Somerset, Frontenac, Seyval blanc; (ii) susceptible – Muscat de Swenson, Geisenheim, La Crescent, Frontenac blanc, Louise Swanson, Delisle; and (iii) highly susceptible – Swenson White, Vandal Cliche, Traminette, and Marquette. Knowledge of the susceptibility of grape cultivars to anthracnose will help growers to make prudent cultivar choices when new vineyards are established. Accepted for publication 31 May 2011. Published 5 August 2011.
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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".