Performance and total tract nutrient digestibility of growing pigs fed hulless low phytate barley
Bibliographic record
Abstract
Woyengo, T. A., Akinremi, O. O., Rossnagel, B. G. and Nyachoti, C. M. 2012. Performance and total tract nutrient digestibility of growing pigs fed hulless low phytate barley. Can. J. Anim. Sci. 92: 505–511. An experiment was conducted to determine the performance and nutrient digestibility of growing pigs fed hulless low phytate (HLP) barley without or with phytase for 28 d. Twenty-four growing pigs (average initial body weight=25.3 kg) were fed three diets in a completely randomised design. The diets included a regular-hulled barley-based diet, and a HLP barley-based diet without or with phytase at 500 phytase units kg−1. Pigs fed the HLP barley-based diet without phytase had greater (P<0.05) average daily gain (0.911 vs. 0.717 kg), and apparent total tract digestibility of dry matter, energy, P, Mg, and Na were higher (P<0.05) than the regular barley-based diet. Supplementation of phytase to the HLP barley-based diet resulted in improved (P<0.05) feed conversion efficiency (0.506 vs. 0.547 kg kg−1), and apparent total tract digestibility of N, P and K. In conclusion, pigs fed the HLP barley-based diet had higher growth performance and nutrient digestibility than those fed the regular-hulled barley-based diet, indicating that the HLP barley is a better source of nutrients for pigs than regular barley. Phytase supplementation to the HLP barley-based diet can result in a further increase in nutrient utilisation by pigs.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".