Evaluation of a phase-feeding strategy utilizing high-lipid high-fibre byproduct pellets in diets for feedlot steers
Bibliographic record
Abstract
Two studies were conducted to evaluate the provision of high-lipid high-fibre byproduct pellets when used as a partial replacement (60% in Study 1 and 30% in Study 2; HLP) for barley grain and canola meal in finishing diets (BAR). The HLP was fed for the last 49, 98, or 147 d (HLP49, HLP98, and HLP147, respectively) in Study 1, and for the last 60 or 120 d in Study 2 (HLP60 and HLP120, respectively) or the last 60 d with additional canola oil (HLP60CO). The statistical model included the fixed effects of diet, period, and the interaction. Steers fed BAR147 had the greatest average daily gain (ADG) (P < 0.01) and G:F (P = 0.01). The HLP147 had the greatest dry matter intake (DMI) during the first 49 d but least during the last 49 d of the finishing phase (treatment × period; P < 0.01). Hot carcass weight for BAR147 and HLP49 were the heaviest (P = 0.04). In Study 2, DMI and ADG were not affected (P > 0.05), but hot carcass weight was greater for BAR120 and HLP60 than HLP120 and HLP60CO. Inclusion of high-fibre high-lipid byproduct pellets in the latter part of the finishing period may improve carcass yield grade without affecting ADG and G:F.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".