Management of<i>Allium</i>white rot [<i>Sclerotium cepivorum</i>] in onions on organic soil with soil-applied diallyl disulfide and di-<i>N</i>-propyl disulfide
Bibliographic record
Abstract
The efficacy of two sclerotia germination stimulants, diallyl disulfide (DADS, 85.5%; related compounds, 4.5%) and di-N-propyl disulfide (DPDS, 88%; related compounds, 2%) were evaluated in naturally infested field plots and in the greenhouse for reduction in populations of sclerotia and control of onion white rot in organic soil. Field trials evaluated one or two applications of DADS or DPDS injected into soil at 10 L/ha in 500 L/ha of water. To determine the treatment effects on the survival of sclerotia, sacs of 100 sclerotia were exposed to treated and untreated soil in the field and greenhouse. In controlled greenhouse studies, survival of sclerotia was significantly decreased when sclerotia were exposed for 1 to 3 months to soil treated with DADS or DPDS compared with untreated soil. DADS was considerably more effective than DPDS. In field trials, survival of sclerotia decreased after 3 months exposure to DADS-treated soil. Single DADS applications reduced disease incidence on onions at harvest compared with the untreated checks at three of four sites. Following two DADS applications, disease was reduced to below 1% at all four sites. DADS applied to organic soil provided effective control of white rot under conditions of moderate disease pressure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".