Effects of dietary squid viscera meal on growth and cadmium accumulation in tissues of Japanese seabass, Lateolabrax japonicus (Cuvier 1828)
Bibliographic record
Abstract
Cadmium (Cd) is a toxic environmental pollutant with a long biological half-life and can produce both hepatic and renal injuries in mammals and fish. Squid viscera meal (SVM), an effective attractant for aquatic animals, is widely used as an ingredient in aquafeeds. However, SVM is rich in Cd and its complexes. A study was conducted to evaluate the effects of dietary SVM on the growth and Cd deposition in tissues of Japanese seabass, Lateolabrax japonicus. Three practical diets were formulated to contain 0, 50 and 100 g SVM kg−1 diet, respectively, containing 0.21, 7.26 and 12.08 mg Cd kg−1 diet. Each diet was randomly assigned to triplicate groups of 80 Japanese seabass (mean initial weight, 10.89±0.21 g) in floating sea cages (1.5 × 1.5 × 2.0 m). Fish were fed twice daily (06:30 and 16:30 hours) to satiation for 8 weeks. The results showed that there were no significant differences in fish survival among three dietary treatments, but significant higher specific growth rates (SGR) were observed in the fish fed diets with 50 or 100 g SVM kg−1 diet than that from the control group (P<0.05). The Cd concentrations in the kidney, liver and gill were found in a decreasing order at each treatment, and positively correlated with dietary Cd levels. Fish fed diets with 50 and 100 g SVM kg−1 diet had significantly higher Cd accumulations in the kidney (3.25, 5.85 mg kg−1), liver (0.76, 1.26 mg kg−1) and gill (0.42, 0.58 mg kg−1) compared with the control group (0.82, 0.34 and 0.32 mg kg−1 respectively) (P<0.05). The Cd concentration in fish muscle; however, was undetectable in any treatment. Therefore, based on these results, accumulation of Cd in edible tissue (muscle) of farmed Japanese seabass is not a food safety issue. However, long-term feeding of diets with SVM may result in accumulation of Cd in the kidneys, liver and gills of fish.
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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) | 0.000 | 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) | 0.001 | 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".