Black Knot Unraveled: Phenotypic Characterization of Disease Resistance in Japanese Plums
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it