Evaluation of host resistance inducers and conventional products for fire blight management in loquat and quince
Bibliographic record
Abstract
Fire blight disease is one of the most destructive diseases of pome fruits. Due to the lack of effective, non-phytotoxic and publicly acceptable materials for controlling fire blight in pome fruit trees, new strategies to manage Erwinia amylovora fire blight are being sought. The resistance-inducing compounds prohexadione-Ca, harpin protein and benzothiadiazole (acibenzolar-S-methyl), the fertilizer humic acid, the bactericides streptomycin and copper salts, and combinations of copper with chemicals were evaluated for their ability to control fire blight on quince and loquat cultivars. Prohexadione-Ca was applied at a rate of 125 mg L -1 at two shoot lengths (6-12 cm and 15-20 cm), while benzothiadiazole + metalaxyl (135 mg L -1 ) and harpin (50 mg L -1 ) were applied when the shoots measured between 15-20 cm, and again at 30-35 cm. On loquat cv. Cukurgobek, benzothiadiazole + metalaxyl showed about 60% effectiveness. The addition of copper salts reduced the effectiveness of benzothiadiazole + metalaxyl. On quince cultivars, streptomycin ( P ≤ 0.05) was the most effective treatment during both years, followed by the harpin protein alone and in combination with copper salts. Prohexadione-Ca, benzothiadiazole + metalaxyl, and harpin protein applications reduced disease severity on inoculated shoots compared with copper and untreated controls. Prohexadione-Ca reduced both shoot length and shoot blight on the two hosts. Humic acid applications were ineffective in controlling fire blight on loquat and quince cultivars. Quince cv. Eşme showed lower disease severity than cv. Ekmek ( P ≤ 0.05). The use of resistance-inducing substances during the early phase of shoot growth may offer a means of managing the shoot blight phase of fire blight disease on quince and loquat.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".