Components of Maize Crop as a Function of Doses of Polymerized Urea
Bibliographic record
Abstract
The efficiency of nitrogen fertilizer applications such as urea is reduced as a function of volatilization and leaching losses. For this reason, the producers have opted for the use of polymerized urea. Therefore, the objective of this work was to evaluate the production components in maize culture as a function of doses of polymerized urea. The experimental design was a 5 × 3 factorial, totaling fifteen treatments, corresponding to five doses Polyblen® 39% N of polymerized urea (0, 250, 500, 750 and 1000 kg ha-1) and three cultivars of maize (30A37, MG580 and MG600). The variables were analyzed after harvest, being: plant height, ear height commercial, stem diameter, leaf area, plant leaf area, ear diameter without husks, ear diameter with husks, ear length without husks, ear length with husks, number of grains per row, number of grains per ear, grain yield, number of rows per ear, 100-grain weight. Regarding the production components in the corn crop, the presence of Polyblen® polymerized urea influenced all variables analyzed. While varieties 30A37, MG580 and MG600 demonstrated a significant increase in their productivity averages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".