Effects of viscosity and fermentability of dietary fibre on nutrient digestibility and digesta characteristics in ileal-cannulated grower pigs
Bibliographic record
Abstract
Relative contributions of two functional properties, viscosity and fermentability of dietary fibre, on apparent ileal digestibility (AID), apparent total tract digestibility (ATTD), digesta passage rate, N retention and SCFA concentration have not been established. Thus, eight ileal-cannulated pigs randomised in a double 4 × 4 Latin square were fed four diets based on maize starch and casein supplemented with 5 % of actual fibre in a 2 × 2 factorial arrangement: low-fermentable, low-viscous cellulose (CEL); low-fermentable, high-viscous carboxymethylcellulose (CMC); high-fermentable, low-viscous oat β-glucan (LBG); high-fermentable, high-viscous oat β-glucan (HBG). Viscosity and fermentability interacted to affect (P < 0·001) digesta viscosity and AID and ATTD of nutrients. These properties tended to interact to affect (P < 0·10) digesta passage rate and butyrate. Pigs fed the CMC diet had the lowest (P < 0·05) digesta passage rate and the highest (P < 0·001) AID of energy, crude protein and DM, and ATTD of energy and DM. Post-ileal DM digestibility was highest (P < 0·001) for pigs fed the CEL and HBG diets. Post-ileal DM digestibility had a negative, curvilinear relationship with the AID of energy and crude protein (R2 0·85 and 0·72, respectively; P < 0·001). Digesta viscosity had a less strong relationship with the AID of energy and crude protein (R2 0·45 and 0·36, respectively; P < 0·001). In conclusion, high-viscous, low-fermentable dietary fibre increases the proportion of a diet that is digested in the small intestine by reducing digesta passage rate.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".