A multibiomarker approach in clams (Ruditapes philippinarum) for a toxicological evaluation of dredged sediments
Bibliographic record
Abstract
The Lagoon of Venice is often dredged for channel maintenance. To avoid harmful consequences to the ecosystem, a proper disposal of bottom sediments requires a preliminary evaluation of its potential toxicity before excavation. Here we evaluated the effects of polluted sediments on clams (Ruditapes philippinarum) using a multibiomarker approach. Bivalves were exposed for 3 and 14 days to five sediment samples collected along a navigation canal between Venice historical centre and the industrial area of Porto Marghera. Immunological, antioxidant, detoxification, and neurotoxicity biomarkers were analysed in haemolymph, gill, and digestive gland. As a control, sediment collected far from pollution sources was used. Two experiments were performed to assess potential seasonal/gametogenic influence in clam sensitivity. A different response of clam biomarkers was observed during the two experiments and among sampling sites. Clams' digestive gland resulted to be the most sensitive tissue analysed showing significant differences among sites in all biomarkers analysed. Greater differences were present due to seasonality rather than exposure. The concentrations of metals and organic pollutants increased from the city centre to the industrial area, highlighting the influence that industrial activities had on the lagoon ecosystem. However, bioaccumulation in clams did not follow the same clear pattern, suggesting low bioavailability of compounds due to relatively high organic matter content. Biomarkers modulation was mainly driven by metals, both present in sediments and bioaccumulated. In comparison, effects of organic pollutants on the biomarkers tested were negligible. Other sources of contamination not investigated (e.g. pesticides) were suggested by neurotoxicity biomarkers alteration.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".