The effects of limit feeding a high-energy barley-based diet to backgrounding cattle in western Canada
Bibliographic record
Abstract
A series of three completely randomized design trials were conducted to compare the effects of a limit-fed high-grain diet relative with that of an ad libitum-fed high-forage diet on performance of growing cattle with similar total energy intakes. In trial 1, the ad libitum-fed high-forage diet was formulated to 1.58 and 0.98 Mcal NEm and NEg kg-1 DM. The high-grain diet was formulated to 1.91 Mcal NEm and 1.23 Mcal NEg kg-1 DM. Projected liveweight gains (1.22 kg d-1) and the amount of DM provided to the limit-fed high-grain cattle were based on the NEm and NEg equations for liveweight gain of large-frame steers (NRC 1984). A similar feeding regime was employed for trials 2 and 3; however, it was necessary to reduce energy intake equally after 30 d on feed to control weight gains. In trial 1, the limit-fed high-grain cattle had similar (P > 0.05) ADG and lower (P < 0.05) DMI than the ad libitum-fed high-forage cattle, leading to a 15.4% improvement (P < 0.05) in feed efficiency. Feed efficiency was improved (P < 0.05) by 16.9% in trial 2 and by 21.2% in trial 3 for the limit-fed high-grain cattle, primarily as a result of reduced (P < 0.05) DM intake and similar (P > 0.05) daily gains. Back fat accretion rates were greater (P < 0.05) in the limit-fed high-grain cattle, indicating differences in energy partitioning. The incidence of severe liver abscesses was greater (P < 0.05) for the limit-fed high-grain cattle in trial 2. These results indicate that limit feeding a high-grain diet to backgrounding cattle can be employed to target specific rates of gain and improve feed efficiency although managing acidosis to prevent liver abscesses may be an issue. Key words: Cattle, limit feeding, feed efficiency, liver abscesses
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".