Amino Acids Composition of Maggot, Earthworm, Termite and Chicken Viscera Meals Used as Proteins Sources in Fish Feeding
Bibliographic record
Abstract
The present study was conducted to compare the nutritive value of animal protein sources such as maggot meal (MM), earthworm meal (EM), termite meal (TM) and chicken viscera meal (CM) for fish feeding. Amino acids composition was determined in triplicate by HPLC method. These sources were rich in crude protein (54.30-71.8%), crude fat (10.7-18.7%) and crude fibre (5.7-9.7%). The total amino acid ranged from 18.8 to 57.19 g/100 g of crude protein and the percentages of essential amino acids were 61.58% (MM), 46.21% (EM), 32.02% (TM) and 45.72% (CM). CM was the richest in total acid amino acid (Aspartic acid and Glutamic acid) than other protein sources whereas basic amino acid (Histidine, Lysine and Arginine) was higher in MM than others. Leucine has the most concentrated amino acid in MM, EM and CM whereas there is phenylalanine in TM. Predicted protein efficiency ratio (P-PER 1 and P-PER 2 ) values were (2.16, 2.14) MM, (0.68, 0.67) EM, (0.79, 0.27) TM, and (1.20, 1.13) CM respectively; isoelectric point (pI) ranged from 1.10 to 3.65; chemical index (ICh) values were: (1.07) MM, (0.24) EM, (0.11) TM and (0.61) CM. Consequently, the results showed that MM was better at 76.47%, CM in 17.64% while TM was better at 5.88%. Based on these, maggot, earthworm and chicken viscera would be recommended as alternative protein sources to fish meal, especially maggot for fish feeding.
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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".