Effects of soybean oil plus additional forage and anabolic implant in finishing steers: feedlot performance, carcass composition, and meat quality
Bibliographic record
Abstract
One hundred twenty crossbred steers were allotted to six weight blocks. Within each block, steers were allotted to one of four pens in a randomized complete block design (5 head per pen, 24 total pens). Treatments were low forage control diets (LFC) or high forage diets supplemented with soybean oil (HFO), without or with anabolic implant in 2 × 2 factorial arrangement. As compared with LFC, HFO reduced dry matter intake and average daily gain, without affecting the gain:feed ratio. Feeding HFO also decreased dressing yield and backfat thickness, with no impact on the longissimus dorsi area and Warner-Bratzler shear force. Meat from steers fed HFO contained greater relative proportion of cis-9, cis-12 18:2, cis-9, trans-11 18:2, and cis-9, cis-12, cis-15 18:3 as compared with LFC. Implanted steers had greater dry matter intake, average daily gain, and gain:feed ratio. Implants improved dressing yield, tended to increase the longissimus dorsi area, decreased backfat thickness, and increased meat Warner-Bratzler shear force. Meat from implanted steers contained greater relative concentration of cis-9, cis-12 18:2 and cis-9, cis-12, cis-15 18:3, without affecting cis-9, trans-11 18:2, as compared with non-implanted animals. No interaction of diet by implant was observed for these variables.
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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".