Effect of Different Feed Ingredients on Growth and Level of Intestinal Enzyme Secretions in Juvenile <i>Labeo rohita</i>, <i>Catla catla</i>, <i>Cirrhinus mrigala</i> and <i>Hypophthalmicthys molitrix</i>
Bibliographic record
Abstract
Feed ingredients are the basic units in feed formulation. Behavior of individual ingredient dictates the overall performance of feed. Therefore, current studies were conducted to investigate the biological value of each ingredient in both Chinese and Indian major carps before their merger into feed formula to enhance fish growth and cut down feed cost. Trial contained two treatments and a control randomly received two glass aquaria (3 × 2 × 2 ft) with 10 fish in each. Fishes in control group were fed on rice polish, T1 on soybean meal and T2 on cotton seed meal @ 3 % of wet biomass of fish for 30 days. Labeo rohita , Hpophthalmichthys molitrix and Cirrhinus mrigala gained maximum weight on rice polish whereas Catla catla on soybean meal. Amylase concentrations were similar in fishes fed on rise polish except Hypophthalmichthys molitrix which secreted significantly low amylases. Values of lipase were the highest in Catla catla when fed on soybean meal. Cirrhinus mrigala did equally well on all ingredients while Hypophthalmichthys molitrix did very poorly. Protease concentrations slightly varied but variations were prominent from species to species. Protease concentrations in Catla catla were similar when fed on rice polish and soybean meal, however, Cirrhinus mrigala displayed higher protease concentrations when fed on cotton seed meal. These studies reveal that various fish species respond to a variety of ingredients in its own way hence acceptability and digestibility criteria should be given due importance during ingredient selection and feed formulation for particular fish species.
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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) | 0.002 | 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".