Weeds on Soybeans Crop After the Application of the Association of the Herbicides Imazapic + Imazapyr on Different Liming Rates in a No-till Cropping System
Bibliographic record
Abstract
The repeated use of the herbicide glyphosate has selected weed resistant species to this molecule. The combination of the tank-mix imazapic + imazapyr (Cultivance® technology) turns out being an alternative on the management of glyphosate resistant weeds. The interaction of these molecules with the soil’s chemical properties with the spraying frequency, and the weed diversity are yet unknown. This study evaluated the effects of liming at the weed incidence on the soybeans crop treated with the association of herbicides imazapic + imazapyr in a no-till cropping system. The experiment was installed at the field in a RCBD with four replications. The experiment was conducted in a factorial arrangement 5 × 2 with five rates of calcitic limestone (0, 2.5, 5, 12.5, and 30 ton/ha) and two corresponding to the presence or absence of the herbicides imazapic + imazapyr (rate of 100 g/ha of the commercial product Soyvance®) sprayed in a spray-plant system. After 40 months of surface-liming, the soybean cultivar Lancer® was planted in a no-till field, and it was evaluated: frequency and abundance of weeds, and the chemical soil parameters: pH, Ca, H+Al, and Mg at the depth of 0-10 cm. The most abundant weeds observed were: Desmodium spp., Schlechtendalia luzulifolia, Digitaria horizontalis, Raphanus sativus and Cyperus spp., with predominance of dicot species. In conclusion, as the surface-liming rate was increased, the greater the frequency of dicot weeds, and the lesser the monocots were found in the area.
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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".