Nutrient digestibility, performance and carcass traits of growing–finishing pigs fed diets containing graded levels of dehydrated lucerne meal
Bibliographic record
Abstract
Abstract BACKGROUND: Plant breeders have attempted to improve the nutritonal value of lucerne (alfalfa) by selecting for higher protein and lower fibre concentrations. Although targeted at ruminants, such changes could also improve the nutritional value of lucerne for monogastrics. The objective of this study was to determine the effects of graded levels of dehydrated lucerne meal on nutrient digestibility, performance and carcass traits of swine. RESULTS: The digestibility of dry matter, protein and energy declined linearly (P < 0.05) as the level of lucerne meal in the diet increased. Including lucerne meal at levels greater than 75 g kg−1 was detrimental to the growth rate of pigs during the growing period. During the finishing period, inclusion of lucerne meal at 75 and 150 g kg−1 resulted in improvements in weight gain and feed intake. Carcass traits were generally unaffected by lucerne inclusion. CONCLUSION: Lucerne meal may have greater potential for inclusion in diets fed to growing–finishing pigs than previously realized. To maximize pig performance, lucerne meal should be limited to less than 75 g kg−1 diet during the growing period, while it is possible to go as high as 150 g kg−1 diet during the finishing period without detrimental effects on performance. Copyright © 2008 Society of Chemical Industry
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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) | 0.000 | 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 | 0.000 |
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".