Diet Affects Muscle Quality and Growth Traits of Grass Carp (Ctenopharyngodon idellus): A Comparison Between Grass and Artificial Feed
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Bibliographic record
Abstract
Fish muscle, the main edible parts with high protein level and low fat level, is consumed worldwide. Diet contributes greatly to fish growth performance and muscle quality. In order to elucidate the correlation between diet and muscle quality, the same batch of juvenile grass carp (Ctenopharyngodon idellus) were divided into two groups and fed with either grass (Lolium perenne, Euphrasia pectinata and Sorghum sudanense) or artificial feed, respectively. However, the different two diets didn’t result in significant differences in all the detected water quality parameters (e.g. Tm, pH, DO, NH3/NH4+-N, NO3--N, NO2-, TN, TP and TOC) between the two experimental groups. After a 4-month culture period, various indexes and expression of myogenic regulatory factor (MRFs) and their related genes were tested. The weight gain of the fish fed with artificial feed (AFG) was nearly 40% higher than the fish fed with grass (GFG). Significantly higher alkaline phosphatase, total cholestrol, high density cholestrol and total protein were detected in GFG as compared to AFG. GFG also showed increased hardness, resilience and shear force in texture profile analysis, with significantly bigger and compact muscle fibers in histologic slices. The fat accumulation was most serious in the abdomen muscle of AFG. Additionally, the expression levels of MyoG, MyoD, IGF-1 and MSTNs were higher, whereas Myf-5, MRF4 and IGF-2 were lower in most positional muscles of GFG as compared to AFG. Overall, these results suggested that feeding grass could promote muscle growth and development by stimulating muscle fiber hypertrophy, as well as significantly enhance the expression of CoL1As. Feeding C. idellus with grass could also improve flesh quality by improving muscle characteristics, enhancing the production of collagen, meanthile, reducing fat accumulation and moisture in muscle, but at the cost of a slower growth.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it