Prickly Sida (Sida spinosa L.), Hemp Sesbania [Sesbania herbacea (Mill.) McVaugh], and Pitted Morningglory (Ipomoea lacunose L.) Response to Selective and Non-Selective Herbicide in Mississippi, USA
Bibliographic record
Abstract
Prickly sida (Sida spinosa), hemp sesbania (Sesbania herbacea), and pitted morningglory (Ipomoea lacunose) are becoming problematic weeds in many crops including corn and soybean. Two separate field experiments, under non-crop conditions, were conducted at the Mississippi State University Delta Research and Extension Center, in Stoneville, MS to evaluate the response of these weeds to eight corn selective herbicides and three non-selective herbicides alone or in combination. The herbicides used were Aatrex (atrazine), Clarity (dicamba), 2,4-D (2,4-D amine), Callisto (mesotrione), Enlist Duo (2,4-D choline + dicamba), Capreno (thiencarbazone + tembotrione), Corvus (thiencarbazone + isoxaflutole), Halex GT (mesotrione + S-metolachlor + glyphosate), Gramoxone (paraquat), Liberty (glufosinate), and Roundup (glyphosate). Halex GT provided only 86% prickly side control 4-wks after application (WAA). Aatrex + Capreno and Aatrex + Corvus were the only treatments provided 99.5% prickly sida control. Liberty, Callisto, Capreno, and Corvus provided 71, 66, and 51, and 46% control of prickly sida, respectively. Hemp sesbania control was 100% from all herbicide applications except Corvus and Roundup. Hemp sesbania control was not satisfactory from Roundup application since this treatment provided only 72% control. By 4 WAA, all herbicide treatments provided 97 to 100% on pitted morningglory control except Corvus and Capreno treatments (72 to 75%). The herbicide management program should be able to provide acceptable weed control (i.e., greater than 95%) but also to prevent weed seed deposition onto the soil seedbank. Consequently, delaying the evolution of weed herbicide resistance will result in herbicide technology preservation for as long as possible by achieving a long-term weed management program.
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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".