Comparative Evaluation of Sweet Orange Waste Meals and Wheat Offal as Fibre Sources in Growing Rabbits Diets
Bibliographic record
Abstract
The study was designed to evaluate the proximate composition and nutritional potential of orange waste meals in rabbits’ diet. The orange wastes (endocarp, mesocarp and whole) meals were gathered from fruit juice processing factory at Export Processing Zone (EPZ) in Calabar Municipality, sundried and milled. Based on the crude protein content, four experimental diets were formulated; Diet one contained wheat offal, serving as control, while Diets 2 to 4 had wheat offal in the control diet replaced by the orange endocarp, mesocarp and the whole orange waste meal, respectively. Forty rabbits of mixed sexes, used for the experiment were weighed and randomly distributed into four 4 groups of Ten rabbits each. Each group was randomly assigned to one of the four (4) experimental diets and fed for 10 weeks. The result showed that the inclusion of orange wastes meal in rabbits diets significantly (P < 0.05) improved the average daily weight gain with the highest (13.99 g/day) in group fed diet 4, average daily feed intake, feed conversion ratio and cost per kg weight gain. Cost of producing a kg of feed and cost of feed consumed by rabbits were significantly reduced. The orange waste meal did not significantly influenced the carcass and internal organs of the animals. It was concluded that, the orange wastes meal could replace wheat offal in growing rabbits’ diet.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".