Effect of lupin (<i>Lupinus angustifolius</i>) as a soybean meal replacement on the performance, meat quality, and blood parameters of broilers
Bibliographic record
Abstract
This study investigated the effects of dietary lupin (LP) as a replacement for soybean meal (SBM) on the performance, meat quality, and blood parameters of broilers. A total of 960 1-day-old Ross 708 broilers were divided into four dietary groups. The four diets were formulated with different levels of dehulled LP content in place of SBM; LP0: 0, LP50: 50, LP100: 100, and LP200: 200 g/kg. There was a trend ( P < 0.10) for reduced weight gain in the LP200 group compared with other groups. The feed conversion ratio was higher ( P < 0.05) in the LP200 group than in the LP0 and LP50 groups. Concerning breast meat characteristics, the lightness color (L*) was lower ( P < 0.05) in the LP200-fed group compared with the LP0 group. Polyunsaturated fatty acids were higher ( P < 0.05) in LP100- and LP200-fed chickens than in LP0-fed chickens. Serum HDL cholesterol was significantly ( P < 0.05) higher in the group fed LP200 compared with the groups fed LP0 and LP50. A higher serum concentration of interleukin (IL)-2 was found in groups fed LP100 and LP200 than in groups fed LP0 and LP50. Our results suggest that LP could be a dose-dependent SBM substitute, and that the optimal level of LP inclusion is approximately 100 g/kg.
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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) | 0.000 | 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 | 0.000 | 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".