BIOMASS SORGHUM SILAGES WITH SUGARCANE
Bibliographic record
Abstract
The storage of forage to be offered at different times of the year are viable alternatives for all production systems, and sorghum biomass has been highlighted for this purpose. As preserved forage, it was hypothesized that sugarcane can contribute to the fermentation process. The objective was to evaluate the inclusion of different levels of sugarcane (0, 20, 40 and 60%) in the silage of three biomass sorghum genotypes (B012, B017 and B018). The material was ensiled using PVC silos and after 60 days the silos were opened and the contents of dry matter, mineral matter, organic matter, crude protein, neutral detergent fiber, acid detergent fiber, hemicellulose, lignin, and hydrogen potential were determined. The experiment was conducted in a completely randomized design, in a factorial scheme with four replications. The data were analyzed through the analysis of variance followed by multiple comparison by Tukey's test (α < 0.05) and linear regression. The biomass sorghum genotypes responded satisfactorily to the fermentation process, resulting in quality silages. However, the inclusion of sugarcane did not improve the quality of the silages, and its inclusion in the silage of the genotypes evaluated is not recommended.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".