Nutritional composition of silage from pearl millet cultivars with the inclusion of soy hulls
Bibliographic record
Abstract
ABSTRACT The nutritional composition of silages from the millet cultivars BRS 1501, ADR 500 and ADR 8010 was evaluated with the inclusion of soy hulls at 0, 3, 6 and 10%. The experimental design was completely randomized in a 3 x 4 factorial scheme with 3 replications. The dry matter content of the silages was affected by the inclusion of the soy hulls, ranging from 16.20% (ADR 8010) to 23.27% (BRS 1501) for the inclusion of 0 and 10% respectively. The levels of crude protein ranged from 9.46 to 10.79%, with significant differences between varieties being seen with the inclusion of 6% and 10% soy hulls. Inclusion of the hulls reduced the mineral matter content of the silages, with a change of 8.62% in the control treatment, up to an average of 6.38% at the level of 10%. The neutral (NDF) and acid (ADF) detergent fiber content did not differ with treatment. The hemicellulose content differed with the level of soy hulls, and ranged from 22.65 to 26.18%. The cellulose content varied from 24.90% (ADR 500), with the inclusion of 6% soy hulls, to 29.80% (ADR 8010) with the inclusion of 10% hulls. The lignin content showed significant differences for the level of hull inclusion, ranging from 1.37% (ADR 8010) with the inclusion of 10% hulls, to 3.71% (BRS 1501), found in the control treatment. The soy hulls increased the dry matter content of silages from the cultivars under evaluation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".