Defoliation Levels Supported in Soybean Crop With No Harm on Productivity in the Municipality of Parauapebas
Why this work is in the frame
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Bibliographic record
Abstract
The reduction in the leaf area is one of the causes in the fall in soybean (Glycine max) productivity as it depends on the production of photoassimilates generated by the leaves, so any factor that interferes in its leaf area may affect the production. The attack of defoliating insects is among such factors. They cause a marked drop in grain yield due to its direct action, therefore, reducing the leaf area, consequently reducing the photosynthetic rate of the plant. The agronomic characteristics of the cultivars may interfere on the level of tolerance of the plant to this type of stress. The objective of this study was to evaluate the influence of defoliation levels on the vegetative and reproductive stages on the development and yield of grains in soybean cultivars. The experimental design was in randomized blocks, in a 2×11×2 factorial scheme, with four replicates. Factors consisted of defoliation stage (vegetative and reproductive), treatment levels (T1-control plant and ten treatments of artificial defoliation) and soybean cultivars (BRS 9090 RR and BRS 8890 RR). The following variables were evaluated: grain yield, dry mass of the pod, leaf dry mass, stem and root dry mass, plant height, stem diameter, number of leaves per plant, length and width of roots. It was observed that the defoliation had a negative effect on the productivity components of the cultivars, with the highest decrease in the reproductive stage, except for the treatment R5, 100% defoliation at the R5 stage, which was also reduced. In relation to the cultivars, the BRS 8890 RR was 27% better in grain yield in relation to BRS 9090 RR.
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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 | 0.000 | 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 it