PSIX-4 Fetal programming – Maternal plane of nutrition effects on progeny performance, feed efficiency, and carcass quality for feedlot steers
Bibliographic record
Abstract
Abstract To evaluate if maternal supplementation of protein and rumen-protected methionine during late gestation affects steer progeny performance and carcass quality, 138 Angus X cows were used in a 3 x 2 factorial arrangement of treatments. Cows were randomly assigned one of three protein treatments (feeding to meet 90%, 100%, or 110% of metabolizable protein (MP) requirements), with(without) 9 g/d of rumen-protected methionine (MET) and fed these isocaloric diets for ~8 weeks before parturition. Steer progeny (n = 56) were assigned to one of seven pens by weight and received a common corn-based grower diet (58% corn silage, 26% alfalfa haylage, 15% soybean meal) for 47 days, followed by a finisher diet (78% high moisture corn, 12% alfalfa haylage, 8% soybean meal) for 115±31.5 days until slaughter. Body weights were recorded biweekly. Monthly ultrasound images of back and rump fats were captured. Organ weights were recorded at the time of slaughter, with carcass and meat quality recorded 24 to 48 hours later. Data were analyzed using PROC GLIMMIX in SAS, with maternal treatment as the fixed effect, and pen as the random effect. Feeding to meet 90% MP requirements resulted in heavier calf weaning weights compared to 100% and 110% (262, 251, and 245 ±13.9 kg, respectively; P = 0.01). Maternal nutritional treatment did not affect average daily gain, dry matter intake, feed conversion during finishing or organ weights. Increasing dietary MP content from 90%, 100% to 110% reduced grade fat thickness (15.5, 14.7, and 11.8 ±1.11 mm, respectively; P = 0.04) and yield grades (2.7, 2.9, and 2.3 ±0.17, respectively; P = 0.04), but increased lean meat yield (49.5, 49.4, and 50.6 ±0.3%, respectively; P = 0.04). These data suggest that maternal MP content may alter steer progeny carcass traits while maintaining animal performance during the finishing period.
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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) | 0.000 | 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) | 0.002 | 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".