Evaluation of Diets with Inclusion of Hydrothermal, Phytase, and Organic Acid Pretreated Canola Meal on Nutrient Digestibility in Swine
Bibliographic record
Abstract
The objective of this study was to evaluate the effects of dietary inclusion of hydrothermal, phytase, and organic acid pretreated canola meal (CM) on nutrient digestibility in swine. A basal diet barley, wheat, and CM based, was formulated. Four diets containing 30% hydrothermal pretreated CM with 50% moisture and 40°C, phytase (1000 FTU/kg activity), and either citric acid (CA), malic acid (MA), or lactic acid (LA), and a control containing pretreated CM without phytase or organic acid were fed to 12 ileal-cannulated finisher pigs (initial average BW 105.3 ± 2.7 kg) in a completely randomized design over two periods of 9 days per period. Nutrient composition and phytate content of the diets, ileal digesta, and feces were analyzed and apparent ileal and total tract digestibilities were determined. The inclusion of hydrothermal, phytase, and CA or MA pretreated CM in the diet decreased phytate P (by up to 38.6%) (p < 0.05), consequently increasing available P (by up to 55.6%). Apparent ileal digestibility (AID) was improved for P by 19.9 ‒ 35.1% units and apparent total tract digestibility of DM by 10.3 ‒ 14.8% units, of protein by 6.6 ‒ 12.1% units, and of gross energy by 12 ‒ 17% units across the treatments (p < 0.05), while AID of CP for MA treatment was up by 4.7% units (p < 0.05) relative to the control, indicating improved diet utilization, thus reduced excretion to the environment.
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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 | 0.001 |
| 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 | 0.001 | 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".