84 Effect of incorporating forage pea (c.v. Pisium sativum L.) into cereal hay on ruminal fermentation and apparent digestibility when fed to beef heifers
Bibliographic record
Abstract
Abstract The objective was to evaluate the effect of inclusion rate of pea hay in barley and oat hay when fed to beef cattle. Six ruminally-cannulated heifers (407 ± 38 kg) were used in a 6 × 6 Latin square with a 2 × 3 factorial design and 25-d periods. Treatments included barley or oat hay blended with pea hay to achieve inclusion rates of 0, 15, or 30% (DM basis). Pea inclusion increased DMI (P = 0.03) by 0.75 kg/d, and generally reduced sorting (P ≤ 0.006) against NDF and ADF compared to the cereal-only treatments. Pea inclusion decreased CP digestibility by 2.87% relative to cereal-only treatments, but did not affect the predicted microbial protein supply or nitrogen retention (P ≥ 0.77). Pea inclusion did not affect total ruminal SCFA concentration, but increased the molar proportions of acetate and butyrate and decreased the molar proportion of propionate (P ≤ 0.01). While cereal type did not affect DMI (P = 0.36) or total SCFA concentration (P = 0.61), use of oat hay improved DM digestibility (67.73 vs. 63.22% for oat and barley, respectively; P < 0.001) and increased nitrogen retention (P = 0.03) when compared to barley. Overall, incorporating pea into cereal hay increased DMI, reduced CP digestibility, and altered ruminal fermentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".