Comparison of canola meal versus soybean meal on growth performance and carcass quality of feedlot cattle
Bibliographic record
Abstract
Canola meal (CM) or soybean meal (SBM) with or without wheat dried distillers’ grains with solubles (WDDGS) were evaluated in two trials. Trial 1 was a 95-day backgrounding program where 398 steers (288 kg) were fed a barley-based diet with either CM, SBM, CM + WDDGS, or SBM + WDDGS as protein supplements in a completely randomized design (CRD). Trial 2 had 300 steers (305 kg) fed barley-based diets in a 61-day backgrounding and 147-day finishing program evaluating same supplements in a completely randomized design. Final BW, ADG, and DMI did not differ ( p > 0.05) in Trial 1. In Trial 2, CM + WDDGS cattle had greater ( p = 0.01) backgrounding DMI than SBM cattle. Steers fed CM had the lowest finishing ( p = 0.03) and overall ( p = 0.05) ADG compared to steers fed SBM. WDDGS improved dressing percentage ( p = 0.05) but decreased number of Canada AAA carcasses ( p = 0.05). Cattle fed CM + WDDGS had the greatest ( p = 0.02) subcutaneous fat depth. WDDGS in the diet increased total PUFA concentration ( p = 0.01) and reduced trans-10: trans-11 ratio ( p = 0.02) but did not reduce the ratio to be biologically significant. The study suggests that CM, SBM, and WDDGS are effective protein supplements for beef cattle.
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 | 0.001 | 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) | 0.001 | 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".