An extended quantitative weight of evidence with uncertainty evaluation for the risk assessment of dredged sediment
Bibliographic record
Abstract
The sustainable management of dredged material requires robust risk characterization of contaminated sediment to protect both dredging areas and destination sites. However, integrated frameworks for assessing sediment quality prior to management are rarely applied. This study presents a paradigmatic case using a quantitative Weight of Evidence (WoE) approach on sediments from a canal in the Venice Lagoon (Italy), designated for future dredging. The aim is to assess whether integrating biological and chemical lines of evidence (LoEs) provides a more robust framework for dredged material risk assessment and management. Multiple LoEs were integrated, including chemical analyses of inorganic and organic contaminants, ecotoxicological bioassays, bioaccumulation tests, biomarker responses, and a novel transcriptomics LoE. Uncertainty in LoE integration was addressed probabilistically to quantify confidence in the final risk assessment. Results revealed a spatial gradient in sediment quality, with higher degradation near the industrial area. However, biological data indicated potential toxicity in sediments far from non-industrial sites that chemical analyses alone failed to detect. This discrepancy suggests the possible presence of not-targeted contaminants or other unknown factors, warranting further investigation. The study emphasizes the importance of assessing sediment effects across biological levels, tracing hazards to specific LoEs, and explicitly addressing uncertainty: these are three key steps for effective sediment risk assessment and informed decision-making.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".