Weed Control and Selectivity to Post-Applied Herbicides in Eucalyptus
Bibliographic record
Abstract
The objective of this study was to evaluate the selectivity of fluazifop-p-butyl and haloxyfop-R methyl ester on Eucalyptus urograndis (clone GG100), as well as the use of fluazifop-p-butyl for control of Panicum maximum and Urochloa brizantha. Two experiments were conducted in 15-liter capacity pots, in a completely randomized design with four replications. The first experiment consisted of seven treatments, in which fluazifop-p-butyl and haloxyfop-R methyl ester were sprayed at 15, 30 and 37 days after planting (DAP) and a control plot without application. In the second experiment, the treatments consisted of a factorial 4 × 2 (four application periods and two weed species), in which three seedlings of P. maximum or U. brizantha were transplanted per pot. In both experiments, at 90 DAP, plant height, stem diameter, leaf area and total dry matter of eucalyptus were evaluated. In the second experiment, besides the morphological parameters, the percentage of weed control was evaluated. The data was submitted to analysis of variance by F test, and the means compared by Tukey test at the level of 5% of probability. Both herbicides did not cause visual effects of phytointoxication in eucalyptus, but haloxyfop-R methyl ester was not selective to clone GG100 (E. urograndis). Fluazifop-p-butyl was selective to clone GG100, providing better control in the first application period (15 DAP) but only for P. maximum, which negatively affected the initial development of eucalyptus, while U. brizantha was not efficiently controlled with the usage of fluazifop-p-butyl.
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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) | 0.000 | 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 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".