Leaf Scorch Epidemics Reduce Vegetative Growth and Fruit Yield of `Kent' Strawberry
Bibliographic record
Abstract
The relationship between severity of leaf scorch epidemics, caused by Diplocarpon earlianum , and components of vegetative growth and fruit yield was examined in `Kent' strawberry ( Fragaria × ananassa Duch.). Plants were treated in July with six densities of initial inoculum of the pathogen, and severity of leaf scorch was assessed at 2-week intervals from late July to late October. After an analysis of vegetative growth in late October, plants were overwintered in the field or grown in a greenhouse, and later assessed for yield components. Relationships between area under the disease progress curve (AUDPC) and plant growth and yield components were examined by regression analysis. Significant negative linear relationships were found between AUDPC values and number of green leaves, leaf area, leaf dry weight, crown number, crown dry mass, and root dry weight. Significant negative relationships were also found between AUDPC values and number of flowers and inflorescences, and total and marketable berries in the subsequent season, in plants maintained in the field or in the greenhouse. Mean berry weight was not significantly affected. Reduction in the number of crowns in plants affected by leaf scorch was a major factor limiting the yield of diseased plants. In an analysis of regrowth at seven weeks after fruit harvest, a significant negative linear relationships was found between AUDPC values and each growth component except crown dry weight. Collectively, the data provide a rationale to optimize timing of treatments, such as chemical fungicides or microbial agents, to control leaf scorch in August, September and October and thereby promote berry yield in the subsequent season.
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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".