Chemical Composition and Energy Yield of Elephant-Grass Biomass as Function of Five Different Production Ages
Bibliographic record
Abstract
Elephant grass has high biomass production, with qualities suitable for conversion into bioenergy, but has long been used exclusively for animal feed and only in recent years has it become an energetic alternative. Therefore, it is necessary to select genotypes with potential for energy production. This study evaluated the effect of five harvest times (8, 12, 16, 20, and 24 weeks) on the yield and chemical composition related to biomass quality through combined polynomial regression analyses of the following elephant grass genotypes: Cubano Pinda, Mercker 86-México, Pusa Napier n°1, Mole de Volta Grande, P-241-Piracicaba, and King Grass. A completely randomized design with three replicates, in a split-plot arrangement, was adopted, including two factors (plots = genotypes, subplots = harvest times). The evaluated variables were whole-plant dry matter yield, in t ha-1 (DMY), percentage of neutral detergent fiber (%NDF), and percentage of acid detergent fiber (%ADF). The elephant-grass genotypes Cubano de Pinda, Mercker 86-México, and P-241-Piracicaba showed a linear first-degree effect as a function of the harvest intervals, indicating that they did not reach their maximum production potential. Genotypes Pusa Napier n°1, Mole de Volta Grande, and King Grass, in turn, had a linear second-degree effect. For the NDF variable, all genotypes showed a significant linear second-degree effect as a function of the harvest intervals, except P-241-Piracicaba, for which no regression was observed. For this genotype, there was a significant linear first-degree effect on the %ADF variable.
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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".