361 Evaluating the effect of liver copper concentration on lubabegron fumarate-induced performance and carcass characteristics of finishing steers
Bibliographic record
Abstract
Abstract The objective of this study was to evaluate the effect of copper (Cu) status on response to lubabegron fumarate (Experior, Elanco, Greenfield, IN). Angus crossbred steers [n = 48; body weight (BW) = 469 ± 31 kg] were enrolled in a 77-d finishing study, housed in pens of 6, with a GrowSafe bunk (GrowSafe Systems, Ltd, Airdire, AB, Canada) in each pen and implanted (Revalor-200, Merck Animal Health, Madison, NJ; 200 mg TBA + 20 mg E2) on d 0. A 2 × 2 factorial arrangement was applied to Cu status (deficient, DEF, no supplemental Cu; or adequate, ADE, 10 mg supplemental Cu/kg DM as CuSO4) and Experior (36 mg·steer⁻¹·d⁻¹, EXP; or no Experior, NOEXP). A prior trial resulted in steers with distinct liver Cu (DEF and ADE were 42 ± 21 and 193 ± 31 mg Cu/kg DM, respectively). Experior was fed from d 37 to d 73, and cattle harvested on d 78. Body weights were collected on d -1, 0, 37, 55 (mid-Experior), 76 and 77. Blood was collected on d 0, 37, 55, and 76 for serum urea nitrogen (SUN), non-esterified fatty acids (NEFA), glucose, and insulin. Data were analyzed in Proc Mixed of SAS as a factorial design, with steer as the experimental unit (n = 12 per treatment). From d 0-37, DEF daily gain and feed efficiency (G:F) were greater than ADE (P < 0.01), while dry matter intake (DMI) did not differ (P = 0.36). Overall DMI was greater in ADE than DEF (P = 0.04) and not affected by Experior (P = 0.13). Carcass-adjusted average daily gain was not affected by Cu or Experior (P ≥ 0.21). Deficient steers had greater carcass-adjusted G:F than ADE (P = 0.02). Hot carcass weight was greater in EXP (P = 0.04), but post-hoc analysis of the Cu x Experior effect (P = 0.2) reveals this was driven by DEF-EXP (15 kg over DEF-NOEXP) rather than ADE-EXP (4 kg over ADE-NOEXP). Marbling tended to be greater in ADE (P = 0.06) and was unaffected by Experior (P = 0.14). The revised quantitative insulin sensitivity check index displayed an EXP x Day effect (P < 0.01) where NOEXP was consistent across days and EXP had greater insulin sensitivity during the Experior period. Serum urea nitrogen was affected by Cu × Experior × Day (P = 0.01), where DEF-NOEXP had lesser d 0 SUN than all others, and EXP decreased on d 55 while NOEXP increased steadily during the finishing phase; by d 76 DEF-EXP remain lesser than NOEXP treatments with ADE-EXP being similar to all others. These data suggest Cu concentration influences steer response to lubabegron fumarate, and more research is needed to determine the cause of this.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".