Temperature Prior to Harvest Influences the Incidence and Severity of Clubroot on Two Asian Brassica Vegetables
Bibliographic record
Abstract
Shanghai pak choy [ Brassica rapa L. subsp. chinensis (Rupr.) var. communis Tsen and Lee] and Chinese flowering cabbage [Yow choy; B. rapa L. subsp. chinensis (Rupr.) var. utilis Tsen and Lee] were seeded into organic (muck) soil naturally infested with the clubroot pathogen ( Plasmodiophora brassicae Woronin) at the University of Guelph Muck Crops Research Station, Ontario, Canada, in June, July, and Aug. 2001 and May, June, July, and Aug. 2002. At harvest, clubroot incidence and disease severity index (DSI) were assessed. Data from 17 different seedings at the research station over 4 years were used to compare the relationship between disease incidence and DSI and weather conditions during crop development. Clubroot incidence and severity were highest for crops harvested in July and August and lowest for crops harvested in October. Mean air temperatures during crop development ranged from 15 to 22 °C and were positively correlated with clubroot incidence and severity for both pak choy ( r = 0.68) and flowering cabbage ( r = 0.73). The strongest correlations occurred between air temperatures and disease severity over the final 10 d before harvest ( r = 0.82 for pak choy; r = 0.84 for flowering cabbage). The research suggests that clubroot damage in Asian Brassica vegetables could be minimized by seeding in early spring and late summer in areas infested with P. brassicae .
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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".