Maize Sowing Speeds and Seed-Metering Mechanisms
Bibliographic record
Abstract
The intensifying use of machines in agriculture to increase operational capacity requires investments in more powerful and automated machines capable of working at higher speeds to meet the demands of agricultural activities. The objective of this study was to evaluate the sowing quality of a second crop maize using a pneumatic sowing machine equipped with two seed-metering devices at different displacement speeds. The statistical design was a randomized block design arranged in 6 × 2 factorial, with 4 replications, totaling 48 experimental plots. Where it was tested two seed-metering mechanisms from different manufacturers denominated A and B, and 6 displacement speeds of approximately 2.0; 4.7; 6.5; 9.1; 10.3 and 12.3 km h-1. The seed-metering mechanisms were compared by mean test while displacement speeds were compared by regression plots. The initial and final plant populations, seed depth, seedling longitudinal distribution (normal, faulty and double spacing) and grain yield were also evaluated. Displacement speed and seed-metering devices showed significant interaction only for the percentages of normal, faulty, and double spacings. The initial and final population presented an isolated effect for both the seed-metering devices and velocities. The seed depth showed an isolated velocity effect. The grain yield showed a significant isolated effect from the analyzed seed-metering devices. The seed-metering device B operating at lower speeds had better performance in the sowing process.
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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 | 0.002 |
| 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) | 0.001 | 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".