Impact of adding water to a barley-based finishing feedlot diet on feed sorting behaviour and ruminal fermentation for growing beef steers
Bibliographic record
Abstract
This study evaluated the effects of adding water to a barley-based feedlot diet on feed intake, feed sorting, ruminal fermentation, and apparent total tract digestibility. Eight ruminally cannulated Hereford crossbred steers were used in a replicated 4 × 4 Latin square design with 4 dietary treatments. Steers received a common diet with aggressively processed dry-rolled barley grain to create a sortable diet. Water was added to the total mixed ration, equating to 0% (CON), 10% (10W), 20% (20W), and 30% (30W) of the barley grain weight. Data were analyzed using the MIXED procedure of SAS with linear and quadratic contrasts. Adding water linearly increased dry matter intake (DMI; P < 0.01) and water intake ( P = 0.04). As water inclusion increased, the sorting index for particles on the pan approached 100% (linear, P < 0.01) indicating greater fine particle consumption. Mean ( P < 0.01) and maximum ruminal pH linearly ( P = 0.02) decreased, while the duration that pH was <5.5 ( P = 0.02) and the concentration of lipopolysaccharide in ruminal fluid increased linearly as water inclusion increased ( P < 0.01). These data suggest that adding water to a barley-based feedlot diet reduces dietary sorting and increases DMI but elevates the risk of ruminal acidosis when using aggressively processed barley grain.
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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) | 0.001 | 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".