Heavy metals in<i>Hediste diversicolor</i>(polychaeta: nereididae) and salt marsh sediments from the lagoon of Venice (Italy)
Bibliographic record
Abstract
The nereid polychaete Hediste diversicolor was frequently used as biomonitor of heavy metals contamination in estuaries. In the present work, the bioaccumulation of five heavy metals (Cd, Cr, Cu, Mn, Pb) in tissues of H. diversicolor collected from six salt marshes in the lagoon of Venice is studied by analysing the relationships between concentrations of the operationally-defined reactive fraction of trace metals in sediments and total concentrations in tissues. The characterisation of sediment samples shows a clear distinction between sites near and far from the Industrial Zone; no analogous pattern is observed for tissue concentrations suggesting that metal bioaccumulation is controlled by complex interactions between metal bioavailability and physiological factors. Considering relationships between metals, strong inverse correlations are observed in polychaetes and sediments for Cu and Cr concentrations suggesting a possible interaction between these two metals. The role played by organic matter (OM) in the availability of metal is highlighted considering inverse relationships found between bioaccumulation factors for Cu, Cr, Cd and OM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".