Incidence and Severity of Cavity Spot of Carrot as Affected by Pigmentation, Temperature, and Rainfall
Bibliographic record
Abstract
Field trials to determine the effect of carrot pigmentation and weather parameters on cavity spot (CS) of carrot were conducted in the Holland/ Bradford Marsh region of Ontario between 2002 and 2009. In all, 23 colored carrot cultivars from the United States Department of Agriculture (USDA) Agricultural Research Service breeding program at the University of Wisconsin (n = 5) and commercial seed companies (n = 18) were seeded in organic soil (pH 6 to 7, 45 to 75% organic matter) in late May to early June and harvested in late October or early November. Carrot roots were assessed for CS severity midseason and postharvest. Evaluations postharvest indicated that the purple pigmented carrot from breeding line 'USDA 106-3' and cultivars 'Purple Rain' and 'Purple Haze' consistently had low CS severity. The orange-pigmented 'USDA 101-23', 'Cellobunch', 'YaYa', and 'Envy' had moderate CS; and the red-pigmented carrot breeding line 'USDA 104-3' and cultivars 'Atomic Red', 'Proline Red', 'Dragon', and an unnamed line from India had high CS. Differences in CS severity in carrot cultivars between evaluations at midseason and postharvest suggest that some carrot cultivars are more susceptible to Pythium spp. inoculum in soil (alloinfection) and others to secondary infection (autoinfection) that can be attributed to the Pythium sp. involved in CS. CS severity was positively correlated with total rainfall 2 and 3 months after seeding, and was negatively correlated with number of days with air temperature ≥30°C 3 and 4 months after seeding. Soil temperature and total rainfall were the best predictors of CS incidence and severity. These results could allow a forecast of disease incidence and severity at 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".