154 The Effect of a Novel Consensus Bacterial 6-Phytase Variant on Ileal Digestibility of Energy in Growing Pigs: Meta-Analysis of 4 Trials
Bibliographic record
Abstract
Abstract This meta-analysis evaluated the effect of a novel consensus bacterial 6-phytase variant (PhyG) on the apparent ileal digestibility (AID) of energy in growing pigs. It was hypothesized that increasing phytase doses improves energy digestibility. The database was constructed using 4 studies (PIC, L 800 × Camborough sows or TN Tempo × TN70 breeds; average body weight of 26.5 kg at sampling), contributing 220 data points. Pigs were fed corn or corn-wheat based diets with soybean meal, canola meal, rice bran, wheat middlings or DDGS. All studies had a negative control (NC) diet deficient in total Ca, dig P, standardized ileal digestible amino acids and without feed phosphates. The analyzed total Ca, total P, phytate P and calculated net energy ranged from 0.46 to 0.53%, 0.37 to 0.43%, 0.24 to 0.30%, and 2,441 to 2,472 kcal/kg, respectively. The NC was supplemented with PhyG at 250, 500, 1,000 or 2,000 FTU/kg. Digesta were collected using ileal T-cannulation (9-10 replicates/treatment, Latin square design). Data were analyzed using the Fit Model platform of JMP 14.0 with trial code as a random effect. Phytase dose response (using analyzed phytase activity) was analyzed with exponential curve fitting. The AID of gross energy and the ileal digestible energy (DE, kcal/kg) improved with 1,000 or 2,000 FTU/kg PhyG treatments vs. NC (P < 0.05, Table 1). Increasing the PhyG dose exponentially increased (P < 0.1) the AID of gross energy (%) and the ileal DE. Ileal DE improvement above NC increased exponentially (P < 0.06) up to 120.8 and 132.6 kcal/kg at 1,000 and 2,000 FTU/kg, respectively. In conclusion, PhyG supplementation improved ileal DE in growing pigs in a dose-dependent manner.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.038 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".