Evaluation of Maize Accessions for Nutrients Composition, Forage and Silage Yields
Bibliographic record
Abstract
An experiment was conducted to evaluate the yield of forage, silage and nutrients composition of two maize accessions: West Atlantic Seed Alliance 1 (WASA1) and an accession from Shika tagged (SHIMAZ). The maize seeds were planted in plots of 5 m x 5 m (25 m2) size with four replicates for each accession. The results showed that, there was no significant (P > 0.05) difference between the accessions for plant height and number of leaves at 4, 6, 8 and 10 weeks after sowing (WAS), but leaf area index (LAI) was significantly (P < 0.05) higher in SHIMAZ accession at 6 and 10 WAS. The number of leaves per plant, green leaves, leaf area and forage yield at 91, 105 and 119 days after sowing (DAS) were not significant (P > 0.05) in the two accessions of maize, but leaf area and LAI were significantly (P < 0.05) higher in WASA1 accession.. The percentage dry-matter (DM) content was significantly (P < 0.05) lower in WASA1, although leaf, and stem ratios were significantly (P < 0.01) higher at various stages of harvest. The crude protein (CP) and nitrogen free-extract (NFE) were significantly (P < 0.01) higher in WASA1 accession while crude fibre (CF) was significantly (P < 0.01) higher in SHIMAZ. The proximate components (ash, EE and CP) and NDF were significantly (P < 0.01) higher in WASA1 silage, but NFE and ADF were higher (P < 0.05) in SHIMAZ accession.
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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 | 0.001 | 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".