Application of Plant Densities in Management Units in the Soybean Cultivation
Bibliographic record
Abstract
The application of management units (MU’s) aims to make economically viable to precision agriculture, making the technique accessible to a greater number of producers. Using MU’s, the experimental area is divided into plots with different productive potentials. In this context, the objective of the experiment was to verify the effectiveness of the area division in MU’s and to define the soybean plant density that provides higher productive efficiency in each MU. For the formation of MU’s it was used the altitude variation and the soil penetration resistance 0-0.1 m in the experimental area, being that the area was divided into 2 MU’s, called MU1 and MU2, and each MU was composed of 8 plots. At planting, 2 plant densities were applied, 214 000 and 257 000 plants ha-1, and each density was applied in 4 plots per MU, using row spacing of 0.70 m. In relation to productivity, there was a significant difference, applying the t-Student test, between MU’s, and the MU2, unit with higher productive potential, located in the highest part in the area, achieved higher productivity; and there was an effect, using the Tukey test, on the application of the 2 different plant densities in the MU’s, being that the densities of 214 000 and 257 000 plants ha-1 reached, respectively, higher productivity in MU2 and MU1.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".