Evaluation of a Petroleum‐Derived Spray Oil for Control of Microdochium Patch and Turfgrass Spring Performance on Nordic Golf Greens
Bibliographic record
Abstract
Core Ideas Mineral oil was as effective as or more effective than fungicides in controling Microdochium patch. Repeated applications of mineral oil in autumn might inhibit turfgrass green‐up in spring. Mineral oil can reduce conventional fungicide use on Nordic golf courses. Greenkeepers are looking for alternatives to fungicides for control of turfgrass diseases. Our objective was to evaluate a petroleum‐derived spray oil with a blue‐green pigment for control of Microdochium patch/pink snow mold (Microdochium nivale) on golf course putting greens with various durations of snow cover. The spray oil was applied at rates 27 or 54 L ha−1 every third week from late August or September to December, either alone, in tank mixture with potassium phosphite (3 kg PO3 ha−1) or in tank mixture with half rate of fungicides approved for turf, in five 1‐yr trials in the Nordic countries. The oil was as effective or more effective than fungicides and gave, on average, 94 and 98% disease control at rates 27 and 54 L ha−1, respectively. Tank mixtures with half rate of prochloraz + propioconazole and fludioxonil did not increase disease suppression in a trial with 79 d snow cover. Phosphite reduced disease severity in one trial only and did not improve disease control or turfgrass quality when tank‐mixed with the oil. The pigment in the spray oil was highly persistent and improved turfgrass greenness except in a trial where the combination of oil and ice cover gave a transitory black color at ice melt. Another trial with long snow cover showed a drop in turfgrass quality in spring as the spray oil prevented normal green‐up. In conclusion, this research shows that a spray oil has the potential to reduce fungicide use on Nordic golf courses.
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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) | 0.001 | 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".