PSIX-5 Fetal programming in an industry applied setting – Effects of feeding methionine during late gestation on progeny performance, feed efficiency, and carcass quality for feedlot steers
Bibliographic record
Abstract
Abstract The objective of this study was to assess if rumen-protected methionine supplementation during gestation would impact male offspring performance and carcass quality in an applied setting. Sixty-seven gestating cows were randomly assigned to control (CON), or methionine (MET) treatments. Cows had ad libitum access to hay from a round bale feeder and were fed once daily in a bunk for approximately eight weeks prior to calving either: 0.75 kg/head/d of supplement pellet supplying 12 g rumen-protected MET/cow/d, or identical pellet with no added MET. The 34 steer progeny (MET n = 18; CON n = 16) were transported to a feedlot, assigned to one of seven pens by weight and fed a 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. Organ weights were recorded at slaughter. Carcass quality, meat quality, and rib composition were recorded 24 to 48 hours after slaughter. Data were analyzed using PROC GLIMMIX in SAS, with treatment as the fixed effect, and pen as the random effect. Supplementation of MET during pregnancy increased weaning weight (MET: 311, CON: 291 ±14.8 kg; P ≤ 0.001), final weight (ME: 668, CON: 631 ±16.8 kg; P = 0.01), and individual dry matter intake (MET: 13, CON: 12 ±0.5 kg/d; P = 0.04) during the finishing phase. There was no effect of maternal methionine supplementation (P ≥ 0.05) for average daily gain. Hot carcass weight was greater for MET steers (MET 372 vs. CON 353 ±9.5 kg; P = 0.03), while organ weights and carcass quality were not affected (P ≥ 0.05) by MET supplementation. This study showed that supplementing methionine during late gestation in an industry applied setting improved some offspring performance measures but did not result in enhanced carcass quality.
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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) | 0.001 | 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".