The effects of a dietary protease‐complex on performance, digestive and immune enzyme activity, and disease resistance of <i>Litopenaeus vannamei</i> fed high plant protein diets
Bibliographic record
Abstract
The effects of a dietary protease-complex on growth performance, body composition, digestive and immune enzyme activity of Litopenaeus vannamei and its resistance to a pathogenic Vibrio parahaemolyticus were assessed in a 9-week trial. A high fish meal diet (HF) containing 200 g kg−1 fish meal and a low fish meal diet (LF) containing 100 g kg−1 fish meal were designed as a positive and negative control respectively. Three other diets (LF+125P, LF+150P, and LF+175P) were manufactured by supplementing graded level of a protease-complex (125, 150 and 175 mg kg−1, respectively) to the LF diet. All diets were formulated to be iso-proteic and iso-energetic. Most performance indices of shrimp fed the LF+175P diets were similar to the HF diet. Among the digestive enzymes, trypsin, lipase and amylase activity in hepatopancreas of shrimp fed LF+175P diets (4576 U mg−1 protein and 16, 32 U g−1 protein, respectively) were higher than those fed the LF diets but lower than the HF diets (P < 0.05). Total superoxide dismutase and polyphenol oxidase contents in both serum and hepatopancreas were higher and serum malondialdehyde content and the cumulative mortality during disease challenge tests were lower for the diets containing the protease-complex than those fed the LF diets (P < 0.05) with no difference with those fed the HF diets.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".