The effect of microbial phytase and feed restriction on protein, fat and ash deposition in growing-finishing pigs
Bibliographic record
Abstract
Forty-eight 35-kg-bodyweight barrows were assigned to treatments in a 2 × 2 factorial arrangement. Main factors were feed intake level (ad libitum or restricted) and supplementation of microbial phytase (with or without). Restricted pigs rece ived 80% of the feed consumed by the corresponding ad libitum group. Phytase-supplemented diets contained 584 phytase units kg-1. Body weight, fat, protein and ash were estimated every 2 wk by dual-energy X-ray absorptiometry. Plasma alpha-amino N concentrations were measured every 30 min during the 6 first postprandial hours at 90 and 132 d of age. Microbial phytase addition reduced feed intake by 6.8% (P < 0.05). Phytase did not affect (P > 0.05) feed, energy and protein efficiencies, but it reduced protein deposition (P < 0.05) and tended to reduce ADG (P < 0.09). Ninety day-old pigs receiving phytase had higher plasma levels of alpha-amino N during the first postprandial hours (P < 0.01) as compared to control pigs. At 132 d of age these effects disappeared. Supplemental phytase may improve alpha-amino acid absorption in growing pigs but the phytase effect on protein deposition suggest that these effect is not necessarily associated with better growth performance when nutrient requirements are satisfied. Key words: Pigs, phytase, feed intake, amino acids, protein deposition
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".