Comparison of glyphosate formulations with and without sequential herbicides for no-till soybean in narrow rows
Bibliographic record
Abstract
Effective control of weeds during early stages of soybean (Glycine max) growth is critical to minimize crop yield reduction. Experiments were conducted to compare weed control and crop yield with two glyphosate formulations (trimethylsulfonium and isopropylamine salts) applied in the fall or spring, either alone or in combination with sequential pre-or postememergence herbicides in soybean cv. ‘Maple Glen’ no-till planted in narrow rows into grain stubble. In six experiments where glyphosate was applied (three in the fall and three in the spring), there was no difference in weed control or in soybean yield between the two glyphosate formulations. Crop yield was improved over glyphosate used alone by addition of metribuzin in all fall experiments and in two of three spring experiments and by addition of linuron in two of three experiments in both fall and spring. An herbicide that controlled annual broadleaf weeds was needed after fall-applied glyphosate in all experiments to achieve maximum soybean yield. Addition of an effective sequential herbicide after spring applied glyphosate improved yields but not to the same extent as noted with the fall applied glyphosate. A pre-emergence residual herbicide, such as metribuzin or linuron, that controls a broad spectrum of weeds is recommended after fall or spring applied glyphosate to maximize soybean yield.
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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".