Effect of Artificial Defoliation in Different Levels on Agronomic Characteristics in Corn Culture
Bibliographic record
Abstract
The objectives of the present work were to evaluate the effects caused by defoliation at different levels in the corn crop, evaluating the agronomic characteristics and yield of maize. The experiment was conducted in the period from September to January, harvest 2016/2017. The experimental design was a randomized block, consisting of five treatments composed of different levels of defoliation of corn plants with four replicates: T1: Witness, without defoliation of plants; T2: Removal of all leaves of the plant; T3: leaves only in the lower third of the plant; T4: Leaves only in the middle third of the plant; T5: Leaves only in the upper third of the plant. Defoliation procedures were performed at the beginning of the VT reproductive stage of maize. The following parameters were evaluated: Spike insertion height; Diameter of the stem; Ear length; Spike diameter; Number of rows of grains on the spike; Number of grains in row; Final Productivity; Weight of a thousand seeds. The results were significant in almost all analyzed variables, where superior results were obtained by the T1 control, followed by T4. It was concluded that the best results were obtained by the control in which there was no defoliation, but there was no significant difference with the results obtained by the treatment in which there were only leaves in the middle third of the plant. From these results it can be affirmed the great importance of the median leaves above and below the spike insertion.
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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 | 0.001 |
| 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".