Brown Bead of Broccoli. I. Response of the Physiological Disorder to Management Practices
Bibliographic record
Abstract
To investigate whether brown bead can be reduced by various cultural practices, a 3-year field study was conducted on a 600-acre broccoli ( Brassica oleracea L., Italica group) farm in southwestern Quebec. Factors studied included N fertilization, soil series, previous crop, season of bed forming, or planting method. Four N treatments were randomly applied to two blocks in 41 fields of `Everest' broccoli: 85-0-0, 85-54-0, 85-54-54, and 85-54-108; the first number indicating N (kg·ha -1 ) applied before planting; the second, N applied 5 weeks after planting; and the last, N applied 7 weeks after planting. Over the 3-year study, brown bead affected 11% of the broccoli heads and accounted for one-third of the rejects. Brown bead severity on individual heads was described on a 0-8 scale. Plots with greater N applications (i.e., 85-54-54, 85-54-108) had significantly ( P < 0.001) lower proportions of plants with brown bead compared with plots with lower N applications. Brown bead incidence reacted similarly from year to year to N fertilization and soil type. However, fertilization interacted with soil type. The less N was applied, the more soil effect was important. Soil effect was maximum at a low N level (85-0-0) with 2.5 times more plants showing brown bead in the Saint Blaise series than in the Sainte Rosalie series. Bed type, previous crop, or planting type did not affect the incidence of brown bead.
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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".