Disease progression of Sclerotinia rot of carrot, caused by<i>Sclerotinia sclerotiorum</i>, from shoot to root before and after harvest
Bibliographic record
Abstract
Sclerotinia rot of carrot, caused by Sclerotinia sclerotiorum, is an economically important disease that causes yield loss in stored carrots. To examine the relationship between infection of leaves in the field and disease development in roots, the histopathology of Sclerotinia rot within carrot crowns was investigated. Petioles of mature greenhouse-grown carrots were inoculated with mycelium of S. sclerotiorum. Disease progression was monitored in carrots in the greenhouse and in carrots that were harvested and placed in cold storage at 4 °C. Disease progressed more rapidly through carrot crowns in storage than in carrots in the greenhouse, and this differential reaction was associated with changes in tissue response to infection. A thick layer of cells that stained red with safranin O and a zone of leaf abscission were observed at the base of infected petioles of carrots that remained in the greenhouse, and these responses were associated with restricted disease progress. Cellular staining occurred rarely and leaf abscission was not detected in carrots that were harvested and placed in cold storage. These results demonstrate a possible preharvest resistance response to S. sclerotiorum in carrot, which was absent in carrots that were harvested and placed in cold-storage conditions. This study contributes to an improvement in understanding of the differential development of this disease in carrots before and after harvest.
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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".