Apparent Digestibility Coefficient and Carcass Composition of Nile Tilapia (<i>Oreochromis niloticus</i>) Fed Processed Duckweed (<i>Lemna paucicostata</i>) Meals
Bibliographic record
Abstract
This study evaluated the apparent digestibility coefficient of processed duckweed based diets and its effect on carcass composition of Oreochromis niloticus. Blanching and sun-drying were employed as the processing methods to reduce antinutrients in the duckweed meal. The experiment was conducted in two outdoor concrete ponds with an area of 5 m x 3.5 m (l × b) and a depth of 1.5 m each, using 27 Hapa nets measuring 1 m 2 each. Nine iso-proteinous diets (D 1 -D 9 ) were formulated using least-cost feed formulation software. Soybean meal was replaced by blanched and sun-dried duckweed meal at 25%, 50%, 75%, and 100% each. A total of 10 fingerlings of O. niloticus (7.46 ± 0.06 g) were stocked per Hapa and fed three times a day at 5% biomass for 24 weeks. Highest apparent protein digestibility coefficient of 92.94% was recorded in the diet containing 75% blanched duckweed meal (D 4 ) while the least value of 86.86% was obtained in the diet with 100% blanched duckweed meal (D 5 ). The fish fed 75% blanched duckweed meal (D 4 ) gave significantly highest ( P ≤0.05) carcass protein of 60.80% followed by D 6 (25% sun-dried duckweed meal) and D 3 (50% blanched duckweed meal) which had similar values of 60.07% and 60.04%, respectively while significantly least value of 44.73% was recorded in the initial carcass protein. The dietary apparent digestibility coefficients obtained in this study suggest that all the blanched and sun-dried duckweed meal can be used to replace soybean meal in Oreochromis niloticus diet without any reduction in protein digestibility and carcass protein contents.
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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 | 0.000 |
| 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".