Serum metabolites and weights of internal organs of broilers fed on varying levels of <i>Acacia angustissima</i> leaf meal
Bibliographic record
Abstract
The objective of this study was to determine the relationship between inclusion levels of Acacia angustissima leaf meal against nutritionally related blood metabolites, activity of liver enzymes, and scaled internal organs in broilers. A total of 120 Ross 308 broiler chicks with initial body weight of 0.90 ± 0.043 kg were randomly allotted to six diets containing different inclusion levels, namely 0, 30, 60, 90, 120, and 150 g kg−1 dry matter of A. angustissima leaf meal. Each inclusion level of A. angustissima leaf meal was replicated four times. Five birds were randomly assigned to each replicate. There was a linear decrease in cholesterol (P < 0.05) with increasing levels of A. angustissima leaf meal. There was a linear increase in scaled gizzard weight (P < 0.05), scaled heart weight (SHW; P < 0.001), and intestine weight (P < 0.001). Scaled spleen weight (P < 0.01) had a positive quadratic relationship with levels of A. angustissima leaf meal. There was a positive quadratic response in alkaline phosphatase (ALP) activity with increasing levels of A. angustissima leaf meal (P < 0.001). The concentration of cholesterol and liver enzymes demonstrates the potential of leaf meals to be incorporated in poultry diets. The optimum inclusion level of A. angustissima was attained at 60 g kg−1 for ALP and 90 g kg−1 for SHW.
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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".