Fire blight susceptibility of select cider apple cultivars
Bibliographic record
Abstract
There is increasing interest in growing apple cultivars ( Malus domestica Borkh.) of European origin for the production of hard cider in Canada; however, little is known about their susceptibility to fire blight (FB). FB can spread rapidly through apple (and pear) orchards causing extensive tree mortality and economic loss. Twenty-eight promising cider cultivars were evaluated over a 7 year period, and in their seventh year of production they were severely naturally infected by an Erwinia amylovora outbreak causing FB. Herein, we report the bloom and harvest dates and tree mortality that developed largely as secondary shoot blight in the summer of 2021. Overall, the cultivars could be classified according to relative susceptibility to FB, based on percentage tree mortality after 7 years: Enterprise (0%); GoldRush and Porter’s Perfection (<20%); Binet Rouge, Kingston Black, Cline Russet, Dabinett, Grimes Golden, Frequin Rouge, Crimson Crisp®, Cox Orange Pippin, and Muscadet De Dieppe (20%–40%); Calville Blanc d’Hiver, Bramley’s Seedling, Yarlington Mill, Michelin, Bulmers Norman, Stoke Red, Golden Russet, Breakwell, Esopus Spitzenberg (50%–90%); Brown Snout, Medaille d’Or, Michelin, Brown’s Apple, Sweet Alford, Tydeman Late, Ashmead’s Kernel, and Tolman (90%–100%). This study highlights the importance of selecting FB tolerant cider cultivars and following best management orchard practices to reduce the spread and prevent infection, which can be achieved by using FB-resistant rootstock, controlling rootstock suckers, FB prediction models, and limited use of antibiotics, biologicals, and careful nitrogen application to regulate tree vigor.
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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 | 0.001 | 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".