Pancreatic mass, cellularity, and α-amylase and trypsin activity in feedlot steers fed diets with increasing corn silage inclusion
Bibliographic record
Abstract
Twenty-four yearling beef steers (initial BW = 535 ± 5.0 kg), predominately of Angus breeding, were used in a randomized complete block design to determine the effect of dietary inclusion of corn silage on pancreatic cellularity, mass, and α-amylase and trypsin activity. Using calan gates, steers were individually fed diets containing 20, 40, 60, or 80% corn silage (DM basis) with the rest of the diet made up of concentrate. Diets were formulated to maintain a constant CP:ME (g Mcal-1) and were fed at 2.1 × NEm requirement. After 28 d on treatment, the two heaviest steers from each treatment were slaughtered per week and pancreata collected. Pancreatic weight (g and g kg-1 BW) and content (kU and U kg-1 BW) of α-amylase activity did not differ among dietary treatments. Concentration (U g-1) of pancreatic α-amylase decreased (P = 0.03) with increasing corn silage inclusion. Pancreatic DNA and RNA content (g and mg kg-1 BW) increased linearly (P ≤ 0.04) with increasing corn silage inclusion. The content of pancreatic trypsin activity responded cubically (P = 0.03). These data indicate that increasing corn silage inclusion does not influence total pancreatic α-amylase activity and that cell number may influence pancreatic α-amylase concentration. Key words: Beef cattle, pancreas, α-amylase, trypsin, forage, concentrate
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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".