2,4-D and Sclerotinia minor to control common dandelion
Bibliographic record
Abstract
Integration of two or more methods in a weed control strategy may produce a positive interaction. In this study, sequential applications of sublethal rates of 2,4-D and the plant pathogen Sclerotinia minor were assessed for integrated control of common dandelion. S. minor was prepared as a granular treatment of fungal-colonized barley grits. Treatments of 2,4-D (25 or 50% of the recommended field rate) and S. minor treatments (20, 40, or 60 g m–2 rate) were applied alone or sequentially with a 3 wk interval. Fourteen days after inoculation (DAI), sequential applications of either rate of 2,4-D with 40 or 60 g m–2 of S. minor caused greater damage than either treatment alone (P = 0.05). By 21 and 28 DAI, control from 60 g m–2 of S. minor alone was equivalent to any of the sequential treatments (P = 0.05). At all assessment times, the combination of either rate of 2,4-D and 20 or 40 g m–2 of S. minor caused damage equivalent to or greater than that caused by 60 g m–2 of S. minor alone (P = 0.05). According to Colby's test for interactions, 19 of 24 assessments of the sequential treatments were synergistic. Therefore, sequential treatments of sublethal rates of 2,4-D and S. minor can interact positively to increase damage. This synergistic interaction may reduce the rate of either component required for adequate levels of control, possibly decreasing the cost or volume of use of herbicides in traditional weed control strategies.
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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) | 0.000 | 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 | 0.001 |
| 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".