Quality and precision processing of barley grain affected intake and digestibility of dry matter in feedlot steers
Bibliographic record
Abstract
Yang, W. Z., Oba, M. and McAllister, T. A. 2013. Quality and precision processing of barley grain affected intake and digestibility of dry matter in feedlot steers. Can. J. Anim. Sci. 93: 251–260. A study was conducted to determine the impact of barley quality and precision processing to account for the impact of variable kernel uniformity on ruminal pH and fermentation, and digestibility in the digestive tract of steers. Eight ruminally cannulated steers were used in a replicated 4×4 Latin square experiment. The four diets consisted of light-weight barley (LB) precision-processed with roller setting based on kernel size; heavy-weight barley (HB) precision-processed; LB and HB grain mixed equal parts then processed at a single roller setting (CON); or LB and HB precision-processed and mixed equal parts (PP). The diets consisted of 10% barley silage and 90% concentrate [dry matter (DM) basis]. Dry matter intake by steers fed LB was greater (P=0.04) than by steers fed HB. Intakes of DM and other nutrients were greater (P=0.04) for steers fed PP than for steers fed CON. There were no differences (P>0.14) in ruminal pH and fermentation among treatments. Digestibility of DM in the total digestive tract tended (P=0.06) to be less with LB than with HB. Precision processing increased digestibility of crude proteion (P=0.04) and acid detergent fibre (P=0.06) resulting in a trend (P=0.10) towards increasing organic matter digestibility as compared with CON. The results suggest that screening of blended barley into more uniform fractions and precision processing of each fraction could increase intake of digestible nutrients for feedlot cattle.
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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 | 0.001 |
| 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 | 0.001 | 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".