Glyphosate Stimulates the Accumulation of N-Compounds, Grain Yield and Seed Vigor in Glyphosate-Resistant Soybean
Bibliographic record
Abstract
Glyphosate-resistant (GR) soybean is widely used in agriculture, however, plants exposed to herbicide show physiological changes. This study investigated the effect of treatments with glyphosate on the metabolism of N compounds, crop yield and physiological quality of seeds in GR soybean. The plants were grown in field experiment, located in the city of Selvíria, MS, Brazil. Glyphosate was applied postemergence at V4 crop stage in a dose-response, including four rates (0; 360; 720 and 1440 g e. a. ha-1) with four replicates. Crop yield, physiological and biochemical features were determined. The results revealed previously unreported stimulant effects of glyphosate on GR soybean plants. Glyphosate changed the ureide shape in leaves, but did not change the concentration of total ureides, indicating maintenance of biological nitrogen fixation in plants exposed to herbicide. Amino acids concentration increased in plants submitted to higher doses. GR soybean showed higher crop yield and seed vigor with increased glyphosate doses. The results of this study indicate that glyphosate does not cause stress to the plant; however, it modulates a distinct response in plant development due to the protective gene inserted. This study can serve as a matrix for additional studies in order to seek clarification of responses of resistant/tolerant plants to glyphosate.
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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".