Assessment of forage inclusion strategies as a means of reducing liver abscesses in finishing feedlot cattle*†
Bibliographic record
Abstract
This study evaluated different strategies of forage inclusion in finishing beef cattle diets and their ef- fects on feed intake, ruminal fermentation and microbiota, blood serum parameters, growth performance, carcass quality, and liver abscesses. Steers (n = 360, 400 ± 29 kg) were stratified by weight and randomly allocated across 24 pens, which were randomly assigned to 1 of 4 dietary treatments (15 steers/pen, 6 pens/treatment) in a completely randomized experiment. Treatments included: (1) positive control (+CTRL) fed a diet (7.5% forage on a diet DM basis) with tylosin (11 mg/kg); (2) negative control (−CTRL; control diet without tylosin); (3) a diet where forage concentration decreased (DECR) every 42 d and was static for the last 84 d (forage represented 15%, 9%, 3%, and 3% of DM, respectively) without tylosin; and (4) a diet where forage concentration increased (INCR), inverse of the DECR without tylosin. The +CTRL steers had greater ADG (1.74 kg/d vs. 1.63 kg/d), shrunk total BW gain (306 vs. 287 kg), and a tendency for greater final BW (705 vs. 687 kg), than than INCR steers. As expected, a diet × period interaction was observed for DMI, but it did not differ among treatments over the full study. Yield scores and rib fat thickness were greater in –CTRL than INCR steers. The percentage of steers with minor liver abscesses tended to be less for +CTRL (51.8%) and DECR (51.8%) compared with −CTRL (62.2%) and INCR (64.3%). Greater dietary con- centrations of forage earlier in the finishing phase, with a subsequent decline thereafter, has the potential to de- crease the proportion of minor liver abscesses similar to typical finishing diets including tylosin, without affecting growth performance or carcass quality.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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 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".