Multivariate Analysis in Corn Cultivars Productivity Submitted to Fertilizations and Row Spacing
Bibliographic record
Abstract
The corn crop is important in various contexts of Brazilian agricultural production, both with respect to economic and social factors. The objective was to verify, through multivariate methods, the productive performance of two corn cultivars as a function of three types of fertilizations and two row spacing, identifying the correlation between the variables and the grouping between the evaluated treatments. The experiment was carried out at Experimental Farm Rafael Fernandes, Mossoró, Brazil. It was adopted a randomized block design at 3 × 2 × 2 factorial experiment with four replications, the treatments consisted of three fertilizations (OF: Organic Fertilization; OMF: Organomineral Fertilization and MF: Mineral Fertilization), two cultivars of corn (Bras 3010 and Potiguar) and two row spacing (80 cm and 50 cm). The highest productivity was found with the use of organic fertilization, in the cultivar Potiguar, in the row spacing of 80 cm. The final population, productivity and the mass of 1000 grains were the components that had the most effect in the evaluation of the data set. Each evaluated cultivar responded differently to the fertilizations and spacing evaluated. The agreement between the results of the cluster analysis and the main component analysis with the analysis of variance shows the adequacy of the multivariate statistical techniques used in this research.
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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 | 0.001 | 0.001 |
| 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".