Removal of supplemental vitamin A from barley-based diets improves marbling in feedlot heifers
Bibliographic record
Abstract
Gibb, D. J., Van Herk, F. H., Mir, P. S., Loerch, S. and McAllister, T. A. 2011. Removal of supplemental vitamin A from barley-based diets improves marbling in feedlot heifers. Can. J. Anim. Sci. 91: 669-674. The objective of this research was to determine if removing supplemental vitamin A from barley-based feedlot diets affects animal performance, health, or carcass quality. Six pens per treatment (10 heifers per pen) were randomly assigned to receive zero (-VA) or 3640 (+VA) IU kg-1 dry matter of supplemental vitamin A in barley-based feedlot diets. Initial serum retinol was similar between treatments (28 µg dL-1; P=0.34), but -VA reduced levels by 40% (30 vs. 50 µg dL-1; P<0.001) by day 217. Removal of supplemental vitamin A reduced dry matter intake during the 58 d backgrounding period (6.93 vs. 7.07 kg d-1; P=0.007) and over the 218-d trial (9.18 vs. 9.35 kg d-1; P<0.001), but had no effect on average daily gain during backgrounding (1.22 kg d-1; P=0.46) or over all (1.46 kg d-1; P=0.15). Based on camera grading, -VA increased degree of marbling (480.6 vs. 439.3; P=0.02) without affecting backfat thickness (0.74 cm; P=0.62). Ultrasound measurements were highly correlated with camera grading, but did not detect treatment difference in marbling score (P=0.99). Results from this study show that the removal of supplemental vitamin A increased marbling without affecting backfat, gains, or animal health.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".