Short Communication: Use of high-lipid by-product pellets as a partial replacement for barley grain and canola meal in finishing diets for beef steers
Bibliographic record
Abstract
Górka, P., McKinnon, J. J. and Penner, G. B. 2013. Short Communication: Use of high-lipid by-product pellets as a partial replacement for barley grain and canola meal in finishing diets for beef steers. Can. J. Anim. Sci. 93: 523-528. The objectives were to determine the effect of the inclusion rate (0%, CON; 30%, HLP30; 60%, HLP60) of high-lipid by-product pellets (HLP) as a partial replacement for barley grain and canola meal on feed intake and growth performance of steers (n=264). Dry matter intake and gain:feed ratio were greater (P<0.01) for HLP30 and HLP60 relative to CON. This study indicates that up to 60% of the barley grain and canola meal in finishing diets for steers can be replaced with HLP, although feed efficiency may be compromised.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".