353 Zinc supplementation prior to transit and transit duration effects on feedlot performance and muscle fatigue of beef steers: Part I – Performance and Muscle Fatigue
Bibliographic record
Abstract
Abstract This study investigated zinc (Zn) as a mitigation strategy for transit-induced muscle fatigue. Recently weaned Angus-cross steers (n = 80) were stratified by body weight (BW = 265 ± 18.0 kg) to a 2×2 factorial. Dietary (DIET) treatments of no supplemental Zn (Zn0) or 100 mg supplemental Zn/kg DM (Zn100; Zn as ZnSO4), were fed for 42 d before transit. Along with a transit duration (DUR) of 8 h (~707 km; 8H) or 18 h (~1,608 km; 18H) on d 0. Upon return, and for 56 d post-transit, all steers received Zn100. Individual feed disappearance was monitored using GrowSafe bunks (1 bunk/pen; 5 steers/pen). Body weights were collected on d -42, -41, -1, 0 (on truck), 1 (off truck), 2, 7, 28, 55, and 56. Blood samples for analysis of non-esterified fatty acid (NEFA) and serum L-lactate were collected on d -1, 1, 2, and 7. Data were analyzed via ProcMixed of SAS (experimental unit = steer; 15 to 20/treatment) as a 2 × 2 factorial, with fixed effects of DIET, DUR, and the interaction. Individual initial (d -42) BW was used as a covariate in BW analysis. Average DMI of d -11 to d -2 (pre-trucking) was used as a covariate in post-transit DMI analysis. Blood parameters were log-transformed and analyzed as repeated measures with repeated effect of day. Pre-transit (d -1) concentrations were used as a covariate. Pre-transit average daily gain (ADG) and dry matter intake (DMI) were greater (P ≤ 0.02) and feed efficiency (gain to feed; G:F) tended to be greater (P = 0.10) in Zn100 vs. Zn0. BW on d -1 and 7 (P = 0.01) was greater for Zn100 than Zn0. There was a DIET effect and DUR tendency on post-transit G:F where Zn0 was greater than Zn100 (P ≤ 0.01) and 18H tended to be greater than 8H (P = 0.05). 56-d post-transit ADG was least for Zn100-8H (DIET×DUR, P = 0.02). Post-transit DMI was greatest for Zn0-8H with Zn100-8H being the lowest and other treatments being not different (DIET × DUR, P = 0.04). Diet did not affect L- lactate or NEFA concentrations (P > 0.13). NEFA was greater for 18H vs. 8H on d 1 and 2 but less on d 7 (DUR×DAY, P < 0.01). There was a DUR×DAY effect (P ≤ 0.01) for L-Lactate where 8H tended to be greater than 18H on d 1, but by d 2 8H was lesser than 18H, and on d 7 they were not different. In conclusion, longer transit duration increases some indicators of muscle fatigue and Zn supplementation improved pre-transit performance. Receiving Zn supplementation after trucking also improved the post-transit performance of those not previously supplemented with Zn.
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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) | 0.001 | 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".