The Impact of Polydimethylsiloxane as a Herbicide Adjuvant for Weed Control in the Lime Citrus × aurantifolia (Christm.) Swingle Plot
Bibliographic record
Abstract
The objective of this research was to evaluate the effect of polydimethylsiloxane (PDMS) CH3[Si(CH3)2O]nSi(CH3)3 as an adjuvant in bioherbicides and synthetic herbicides (chemicals) for weed (Johnson grass S. halepense and star grass Cynodon sp.) management in the lime plot. A total of 20 treatments were conducted, which included bioherbicides and synthetic herbicides, each with three replications, the dose was 50% more polydimethylsiloxane and 100% according to the product label. The experimental design was a randomized block design, with a factorial arrangement with three factors: A (products) with 10 levels, B (adjuvants) with 2 levels and C (evaluation dates) with 5 levels, each block consisted of an area of 5 m by 4 m. The assessment was carried out periodically, every week (5 evaluations), the scale proposed by EWRS was used. The results were in percentages and a 3-factor factor analysis was performed, where A (products) = 10 levels, B (adjuvants) = 2 levels and C (evaluation dates) = 5 levels, and the mean comparison test with Tukey (α ≤ 0.05), using SAS software. According to the results obtained, a significant difference is only presented between factors A (products) and B (adjuvants) (P < .0001). In factor A, the highest levels in weed control were glyphosate, diuron + carfentrazone and ammonium glufosinate with an average of 80.8, 74.0 and 70.8%, respectively. Regarding the interaction between the factors, only the A*B factors were present (P < .0001), only in the glyphosate, BH2 and diuron were the treatments with the highest interaction when using the 50% dose + polydimethylsiloxane as an adjuvant at a dose of 2 ml per 1 L of water with 76.2, 72.93 and 71.33%, respectively. The date when the treatments had the highest weed control effect was at the second and third week. These results underline the effect of polydimethylsiloxane to optimize the use of herbicides, not only in combination with chemical herbicides, but also with bioherbicides, offering an efficient and economic alternative for weed management (Johnson grass S. halepense and star grass Cynodon sp.).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".