Effect of high-fat by-products pellets in finishing diets for steers
Bibliographic record
Abstract
Cereal grain prices have increased in recent years resulting in increased feed costs for feedlot operators. To offset these costs, investigation into alternative energy sources, relative to cereal grains, has been initiated (Marx et al., 2000). Applying single ingredient substitution strategies may limit the use of some byproducts for finishing cattle, such as grain screenings, pea screenings or oat hulls, due to insufficient energy content (Marx et al., 2000) or alternatively may limit the amount of cereal grain that can be replaced. One strategy to eliminate overfeeding of nutrients from high byproduct inclusion rates is to utilize strategic combinations of various byproducts to optimize ruminal and postruminal energy and protein availability (Zenobi et al., 2012). The aim of this study was to determine the effect of including strategically blended high-fat by-product pellets as a partial replacement for barley grain and canola meal in finishing diets for steers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".