PSXII-3 Impact of a fibrolytic enzyme additive on digestibility and performance in the grower and early finisher phases of feedlot cattle
Bibliographic record
Abstract
Abstract The objective of this study was to examine if a fibrolytic enzyme feed additive would improve animal performance and apparent total tract digestibility of fibre in feedlot rations. To meet these objectives 54 steers were assigned to one of three pens by weight and fed a corn-based grower (78.7% corn-silage and 20% dried distillers grains plus solubles, DM basis) diet for 80 days, followed by a finisher diet (60% high moisture corn, 20% DDGS, and 17% alfalfa haylage, DM basis) for 60 days. Steers were randomly assigned to control (CON; n = 27) or enzyme (ENZ; n = 27) treatments, with ENZ steers receiving 0.75 ml/kg DM of the enzyme additive. Every 28 days body weight, ultrasound measures of back and rump fat depths were recorded, and blood was collected via jugular venipuncture. Ruminal pH was monitored using a reticulo-ruminal in-dwelling probe and recorded at five minutes intervals over three weeks each in the grower phase and through the transition. Apparent total tract digestibility was measured using acid-insoluble ash as an internal marker. Data were analyzed as a complete randomized block design using PROC GLIMMIX in SAS, with treatment as a fixed effect, and block as a random effect. Adding ENZ during the grower and early finisher phases did not impact (P ≥ 0.05) animal performance traits (gains, feed intake, feed conversion), blood metabolites, or ruminal pH in grower or finisher periods. However, ENZ significantly (P ≤ 0.05) improved digestibility of dry matter, crude protein, sugar, and net energy of gains. This study has demonstrated that the use of this fibrolytic enzyme in a corn-based feedlot diet improved digestibility of some nutrients, but this did not result in improved steer performance.
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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 | 0.000 |
| 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 | 0.001 | 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".