Effects of Moringa Leaf and Fruit Powder Supplementation on Hematological Parameters and Growth Performance in Kacang Goats
Bibliographic record
Abstract
Livestock producers are currently facing challenges such as scarcity and high prices of concentrate feed, which significantly impact livestock productivity.Therefore, this research aimed to assess the impact of supplementing moringa leaf and fruit powder on hematological parameters, cholesterol levels, weight gain, and carcass weight in female Kacang goats.To conduct the analysis, a Randomized Block Design (RBD) with three treatments was adopted, each repeated four times as groups.The treatments included P0: Concentrate without moringa leaf and fruit powder, P1: Concentrate with the addition of 20% moringa fruit powder, and P2: Concentrate with the addition of 20% moringa leaf powder.The experimental livestock comprised 12 female Kacang goats aged 10-12 months, weighing between 8 kg to 12.5 kg.The observed variables included weight gain, dry matter intake, feed efficiency, leukocyte count, erythrocyte count, hemoglobin levels, hematocrit levels, High-Density Lipoprotein (HDL), Low-Density Lipoprotein (LDL), Triglyceride (TG) levels, carcass weight, and carcass percentage.The analysis of variance (ANOVA) indicated significant effects of supplementing concentrate with moringa leaf and fruit powder on weight gain, HDL, LDL, and TG levels, carcass weight, and carcass percentage in female Kacang goats.These results showed that adding moringa leaf and fruit powder into concentrate could effectively reduce cholesterol levels in goats meat.Additionally, the 20% addition of the powder to concentrate enhanced protein contribution, thereby accelerating livestock weight gain.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".