PSI-35 Corn supplementation of beef cows and its impact on growth performance and carcass outcomes of their progeny.
Bibliographic record
Abstract
To examine the impact of corn supplementation of cows on carcass outcomes of their offspring, forty-seven multiparous Angus beef cows carrying male calves were assigned randomly to two dietary treatments. Treatments were control (CON; n=23) receiving ad libitum access to a low quality, forage-based diet (57.54% TDN, 6.4% CP) TMR, and a supplemented group (SUP; n= 24) receiving corn at 0.02% BW (94.5% TDN, 7.64% CP) in addition to ad libitum access to the basal TMR. Dietary treatments started on d 110 of gestation for 22 wks. Following parturition, cows with their offspring were placed on pasture and managed as a single group. Calves were weaned (~159 d of age) and backgrounded for 250 d. Thereafter, the steers were placed in the feedlot, assigned to 4 pens (blocks) based on BW and offered a 57% corn silage/ 38% barley grain-based ration with growth performance measured at 28-day intervals. Pens 1 through 4 were fed for 69, 83, 96, and 137 d, respectively, to reach a final BW of 615 kg (~16 mo of age) and then sent to a commercial abattoir. Steers from SUP and CON cows did not differ in initial BW, final BW, and ADG, during the finishing period (P > 0.05). No significant difference in carcass quality and yield traits, including subprimal cut yields, bone, lean and fat trim, was observed between treatments (P > 0.05). In addition, differences in the frequency distribution of the Canadian quality and yield grades (P > 0.05) were not observed. Warner-Bratzler shear force values tended to be higher in steers from SUP dams than steers from CON dams (P = 0.07). Overall the results indicate that corn supplementation of cows during the mid to late gestation does not affect growth performance and carcass outcomes of their offspring.
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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".