302 Vitamin and Mineral Supplementation and Rate of Gain in Beef Heifers: Effects on Fetal Trace Mineral Reserves at Day 83 of Gestation
Bibliographic record
Abstract
Abstract Thirty-five crossbred Angus heifers (body weight = 359.5 >± 7.1 kg) were randomly assigned to a 2 × 2 factorial design to evaluate the effects of vitamin and mineral supplementation [VMSUP; supplemented (VTM) vs. unsupplemented (NoVTM)] and rate of gain [GAIN; low gain (LG), 0.28 kg/d vs. moderate gain (MG), 0.79 kg/d] during the first 83 d of gestation on trace mineral concentrations in fetal liver, muscle, and allantoic (ALF) and amniotic (AMF) fluids. The VTM treatment (113 g supplement•heifer-1•d-1) was initiated a minimum 71 d before breeding. At breeding, heifers were either maintained on the basal diet (LG) or received the MG diet by adding a protein/energy supplement to the basal diet. On d 83 of gestation, samples of fetal liver, muscle, ALF, and AMF were collected and analyzed for trace mineral concentrations. In fetal liver, Se, Cu, Mn, and Co concentrations were greater (P ≤ 0.04) for VTM than NoVTM, while Mo and Co greater (P ≤ 0.04) for LG than MG. In fetal muscle, VTM increased (P ≤ 0.02) concentrations of Se and Zn, whereas LG increased (P < 0.01) Zn. In ALF, Mo concentrations were affected (P = 0.03) by a VMSUP × GAIN interaction, with VTM-MG greater than NoVTM-MG; while VTM increased (P < 0.01) concentrations of Se and Co. Trace mineral concentrations were not affected (P ≥ 0.13) in AMF. In conclusion, VTM increased fetal liver Se, Cu, Mn, and Co concentrations; fetal muscle Se and Zn; and ALF Se and Co; while LG increased fetal liver Mo and Co concentrations and fetal muscle Zn. Our results confirm that managerial decisions associated with vitamin and mineral supplementation and rate of gain can alter fetal reserves of trace elements during early pregnancy.
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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".