Digestibility and performance of feeder lambs fed mixed barley grain – barley silage diets with varieties of barley silage selected on the basis of in vitro neutral detergent fibre degradability.
Bibliographic record
Abstract
Barley silage varieties ranked based on in vitro neutral detergent fibre digestibility (NDFD) of commercial silage samples were designated as high-NDFD (H-NDFD, ‘CDC Cowboy’), intermediate-NDFD (I-NDFD, ‘CDC Copeland’), and low-NDFD (L-NDFD, ‘Xena’) and assessed in digestibility and lamb performance experiments. A replicated 3 × 3 Latin square digestibility experiment fed 50:50 silage:concentrate diets [dry matter (DM) basis] to nine rumen fistulated wethers. A growth study used 42 lambs fed 40:60 silage:concentrate diets (DM basis) with carcass traits being assessed in 21 ram lambs. In vitro NDFD of silages did not coincide with the ranking of field silage samples. Intake and digestibility in wethers did not differ (P > 0.05) among varieties. Mean rumen pH was lower (P > 0.05) for wethers fed L-NDFD than H-NDFD, with rumen pH of wethers fed L-NDFD spending more (P < 0.01) time below 6.2, 6.0, and 5.8. Growing lambs fed L-NDFD had lower (P < 0.01) dry matter intake (DMI) than lambs fed I-NDFD. Dressing percentage was higher (P < 0.05) for ram lambs fed L-NDFD than I-NDFD. Selecting barley silage varieties based on improved in vitro NDFD to improve digestibility and lamb performance is difficult due to yearly differences in forage growing conditions and ensiling dynamics.
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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".