A Decision-Making Framework for Sediment Contamination
Bibliographic record
Abstract
A decision-making framework for determining whether or not contaminated sediments are polluted is described. This framework is intended to be sufficiently prescriptive to standardize the decision-making process but without using "cook book" assessments. It emphasizes 4 guidance "rules": (1) sediment chemistry data are only to be used alone for remediation decisions when the costs of further investigation outweigh the costs of remediation and there is agreement among all stakeholders to act; (2) remediation decisions are based primarily on biology; (3) lines of evidence (LOE), such as laboratory toxicity tests and models that contradict the results of properly conducted field surveys, are assumed incorrect; and (4) if the impacts of a remedial alternative will cause more environmental harm than good, then it should not be implemented. Sediments with contaminant concentrations below sediment quality guidelines (SQGs) that predict toxicity toless than 5% of sediment-dwelling infauna and that contain no quantifiable concentrations of substances capable of biomagnifying are excluded from further consideration, as are sediments that do not meet these criteria but have contaminant concentrations equal to or below reference concentrations. Biomagnification potential is initially addressed by conservative (worst case) modeling based on benthos and sediments and, subsequently, by additional food chain data and more realistic assumptions. Toxicity (acute and chronic) and alterations to resident communities are addressed by, respectively, laboratory studies and field observations. The integrative decision point for sediments is a weight of evidence (WOE) matrix combining up to 4 main LOE: chemistry, toxicity, community alteration, and biomagnification potential. Of 16 possible WOE scenarios, 6 result in definite decisions, and 10 require additional assessment. Typically, this framework will be applied to surficial sediments. The possibility that deeper sediments may be uncovered as a result of natural or other processes must also be investigated and may require similar assessment.
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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 | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".