Nutritive values of double-low rapeseed expellers and rapeseed meal with or without supplementation of multi-enzyme in pigs
Bibliographic record
Abstract
Two experiments were conducted to evaluate the effects of adding multi-enzyme on the available energy concentration and standardized ileal digestibility (SID) of amino acids of double-low rapeseed expellers (RSE) and rapeseed meal (RSM) fed to growing pigs. In Experiment 1, a total of 36 growing barrows were fed six diets in a replicated 3 × 2 factorial design. Diets included a corn–soybean meal basal diet, RSE diet, and RSM diet with or without multi-enzyme (1000 U g−1 of cellulase, 10 000 U g−1 of xylanase, 2000 U g−1 of glucanase, and 10 000 U g−1 of protease). In Experiment 2, a total of 30 crossbred pigs were randomly allotted to five diets consisting of a nitrogen-free diet and four cornstarch-based diets containing RSE or RSM with or without multi-enzyme. Multi-enzyme supplementation significantly improved (P < 0.05) the metabolizable energy (ME) value and apparent total tract digestibility (ATTD) of ether extract, neutral detergent fiber, and acid detergent fiber, and it tended to increase (P < 0.10) the ATTD of gross energy and digestible energy in ingredients. With the exception of phenylalanine, multi-enzyme supplementation increased (P < 0.05) the SID of crude protein (CP) and all amino acids (AA) in ingredients. Multi-enzyme supplementation enhanced fiber degradation and increased the ME value and SID of CP as well as most AA in double-low rapeseed co-products fed to 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 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".