Black Knot Unraveled: Phenotypic Characterization of Disease Resistance in Japanese Plums
Bibliographic record
Abstract
Black knot (BK) disease, caused by Apiosporina morbosa (Schwein.) v. Arx, significantly afflicts Japanese plums (Prunus salicina L.), resulting in substantial economic losses due to its destructive invasion of branches and trunks. Phenotyping for disease severity is critical to understanding resistance and susceptibility across diverse genotypes. In this study, 200 Japanese plum trees from a mixed lineage breeding program were phenotyped for BK severity using a rating scale from 0 to 5. Trees were rated by two independent raters and repeated on a second day, in early spring 2023, before leaf emergence, for peak visibility. The rating system was designed to capture varying levels of infection, with 0 representing no symptoms and 5 indicating severe infection with major effects to the tree’s overall health. Compared to data from 2015 and 2018, there was a noticeable increase in the number of heavily diseased trees relative to symptom-free trees. In 2023, the proportion of completely resistant trees remained the same as in 2018, suggesting true resistance. Median scores were calculated from four independent ratings per tree, comprised of two individuals on two different days, minimizing individual biases. Additionally, inter-rater reliability was assessed using the weighted Kappa statistic, which yielded a value of 0.903, indicating strong agreement between raters. This phenotypic assessment provides a robust dataset for correlation with genetic markers and supports further breeding efforts aimed at developing BK-resistant cultivars.
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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".