222 Awardee Talk: What we Know Now: Strategic Zinc Supplementation for Cattle Utilizing Growth Promoting Technologies
Bibliographic record
Abstract
Abstract A 98-d study examined the effects of increasing supplemental Zn on cattle performance, plasma Zn concentrations, and nitrogen metabolism using 92 Angus-crossbred steers (424 ± 28 kg) administered growth-promoting technologies. Steers were implanted with Component TE-200 (Elanco, Greenfield, IN) on d 0 and fed 300 mg‧steer-1‧d-1 of ractopamine hydrochloride (Zoetis, Parsippany, NJ) from d 70-98. Pens were equipped with GrowSafe bunks (GrowSafe Systems Ltd., Airdrie, AB, Canada), and steer served as the experimental unit (n = 22 or 23 steers/treatment). Dietary Zn treatments included 0, 100, 150, and 180 mg Zn/kg dry matter from ZnSO4 (Zn0, Zn100, Zn150, and Zn180, respectively). Data were analyzed via the Mixed and Corr procedures of SAS. Contrast statements tested linear, quadratic, and cubic effects of Zn supplementation and Zn0 vs. Zn supplementation. Day 10 and 70 body weights and d 0-10 and 0-70 average daily gain were linearly increased with increasing Zn supplementation (P ≤ 0.05), and greater for Zn supplemented steers (P ≤ 0.03). Concurrently, d 10 and 69 plasma Zn concentrations linearly increased (P ≤ 0.03). Final body weight, dressing percentage, ribeye area, 12th rib fat, and marbling were not influenced by Zn supplementation (P ≥ 0.11). Hot carcass weight tended to be 7 kg greater for Zn supplemented steers than Zn0 (P = 0.07). Day 10 liver Mn tended to be positively correlated with d 10 liver arginase activity (r = 0.27; P = 0.07) and was positively correlated with d 10 serum urea nitrogen (r = 0.55; P < 0.0001). Neither d 79 liver arginase activity nor serum urea nitrogen were correlated with d 79 liver Mn (P ≥ 0.21). These data suggest increased supplemental Zn is beneficial to growth directly following implant administration and that implants and beta agonists differentially influence nitrogen metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".