Plant Density Effect on Silage Yield of Maize Cultivars
Bibliographic record
Abstract
In order to study of plant density effect on silage yield of new maize hybrids an experiment was conducted in factorial on the basis of randomized completely block design with four replications at agricultural research station in Qarakheil (Qaemshahr) in 2014 in Iran, after wheat harvesting. New hybrids are SC703 and SC705 that were compared with SC704. Four densities were 75,000, 85,000, 95,000 and 105,000 plant per hectar. Eeach plant planted in four rows with intera-row spacing of 75 cm. Silage yield, plant height, ear height, kernel number in ear row, kernel row number,dry silage yield, kernel, stem and leaves protein were measured. Result showed that SC704 had the highest silage yield with 70.96 t/ha. SC705 and SC703 had 64.67 and 60.22 t/ha silage yield. Density of 105,000 and 95,000 plant per hectar had higher silage yields in compare of other densities with 67.04 and 66.20 t/ha respectively. Result showed that SC704 had the highest dry silage yield with 43.19 t/ha. SC705 and SC703 had 38.69 and 36.75 t/ha dry silage yield respectively. Density of 105,000 and 95,000 plant per hectar had higher dry silage yields in compare of other densities with 40.67 and 40.55 t/ha respectively. With increasing of density, crude protein (in kernel, stem and leaves) decreased.
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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 | 0.000 | 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".