Chemical composition and in vitro ruminal degradation of hay and silage from tropical grasses
Bibliographic record
Abstract
Arroquy, J. I., Cornacchione, M. V., Colombatto, D. and Kunst, Jr., C. 2014. Chemical composition and in vitro ruminal degradation of hay and silage from tropical grasses. Can. J. Anim. Sci. 94: 705–715. This study was conducted to evaluate the effects of preservation type on chemical composition and in vitro ruminal degradation of warm-season grasses (WSG). Treatments consisted of two factors (6×2): the first factor was tropical grasses: Cenchrus ciliaris (cv. Biloela, and cv. Molopo), Chloris gayana (cv. Callide and cv. Finecut), Panicum maximum, and Brachiaria bryzanta; and the second factor was preservation type (hay vs. silage). Cell wall, hemicellulose, cellulose, and water-soluble carbohydrate (P<0.05) concentrations were different among WSG. In general, hay or silage altered fiber content compared with fresh. For instance, hemicellulose and cellulose contents were lower in silage than in hay and fresh grass (P<0.05). Gas production rates were higher in silage from 0 to 24 h of fermentation, except at 4 h of incubation. After 24 h, gas production (GP) rate was similar for both preservation types, whilst potential GP was similar between preservation types. However, silage had decreased lag time compared with hay (P<0.01). Silage had greater dry matter disappearance than hay (P<0.05), and gas production yield was similar for grass species and preservation type. Our results indicate that WSG conserved as silage showed beneficial changes in chemical composition and dry matter degradation compared with hay.
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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) | 0.000 | 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".