Effect of incorporating forage pea (<i>Pisum sativum</i> L.) hay into cereal hay on ruminal fermentation and apparent digestibility when fed to beef heifers
Bibliographic record
Abstract
The objective of this study was to evaluate the inclusion rate of pea hay in barley or oat hay diets for beef cattle. Six ruminally cannulated heifers (407 ± 38 kg) were used in a 6 × 6 Latin square (25 d periods) with a 2 × 3 factorial design. Treatments included whole-crop barley or oat hay with pea hay blended in to achieve inclusion rates of 0%, 15%, or 30% (dry matter basis) of the forage. Pea hay inclusion increased dry matter intake (DMI; P = 0.03) by 0.75 kg d−1 relative to diets without pea hay, but the response was not linear or quadratic. Inclusion of pea hay linearly increased mean ruminal pH (P = 0.039), the concentration of butyrate in ruminal fluid (P = 0.013), plasma urea nitrogen (N) concentration (P = 0.001), and quadratically increased ruminal ammonia concentration (P < 0.001). Pea hay inclusion reduced crude protein (CP) digestibility by 2.87% relative to cereal-only treatments (P = 0.025), but did not affect N intake, microbial N, or N excretion. Overall, pea hay inclusion increased DMI, increased ruminal butyrate concentration, but reduced CP digestibility without affecting N balance.
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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 | 0.001 |
| 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".