Bioaccumulation patterns in aquaculture mussels and turbots exposed to different sizes of TiO2NPs
Bibliographic record
Abstract
Titanium dioxide nanoparticles (TiO 2 NPs) are widely used in industry, leading to their presence in the marine environment where they can interact with and harm marine life. This study examines the impact and bioaccumulation of TiO 2 NPs in aquaculture mussels and turbot species. Mussels were exposed for 28 days, and turbot for 90 days, to different concentrations of citrate-coated TiO 2 NPs (25 and 5 nm). Inductively coupled plasma mass spectrometry (ICP-MS) and single-particle-ICP-MS (SP-ICP-MS) were used for Ti determination, and TiO 2 NPs content and size distribution determination. In mussels TiO 2 NPs concentrations reached 2.28 × 10 8 ±5.84 × 10 7 NPs g −1 after exposure to 1.0 mg L −1 of 25 nm TiO 2 NPs for 28 days, and 4.79 × 10 8 ±2.36 × 10 8 NPs g −1 for 1.0 mg L −1 of 5 nm TiO 2 NPs at 21 days, respectively. Bioaccumulation was also observed in mussel shells, which became fragile, with the highest Ti concentration reaching 12.4 ± 3.5 µg g −1 d.w. In turbot, the highest Ti concentration was found in the liver, reaching 1.6 ± 0.4 µg g −1 w.w. at 90 days to the highest dose of 5 nm TiO 2 NPs. Furthermore, turbots expelled Ti through the feces, reaching 41.0 ± 6.6 µg g −1 d.w. The results show the safety of turbot consumption and highlight the need for a correct depuration process of mussels before commercialization.
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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 | 0.000 |
| 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".