Reducing Grain Sorghum (Sorghum bicolor L. Moench) Injury From Postemergence Application of Mesotrione With Dicamba
Bibliographic record
Abstract
Weed management in grain sorghum is limited by the number of herbicide options. A two-year (2017-2018) field study was conducted at the Mississippi State University Delta Research and Extension Center, in Stoneville, MS to evaluate the response of grain sorghum to mesotrione application alone or when tank-mixed with dicamba at the two-leaf and four-leaf growth stage of sorghum. Mesotrione was applied at 0.07 and 0.105 kg ai ha-1 alone or was tank-mixed with dicamba at 0.28 kg ae ha-1. Significant injury to grain sorghum from all herbicide treatments was observed compared with the untreated check. Increase in mesotrione application rate increased injury to grain sorghum from 14 to 19% at two-leaf and from 10 to 24% at the four-leaf stage by 4 weeks after application (WAA) in 2017. Adding dicamba to mesotrione reduced grain sorghum injury in both years. At 4-leaf sorghum application stage, mesotrione applied at 0.07 kg ha-1 resulted in greater grain yield than all other herbicide treatments, except mesotrione (0.105 kg ha-1) + NIS in 2017. Our results indicate that adding dicamba to mesotrione safes grain sorghum from injury caused by mesotrione alone.
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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".