Contaminants at the Sediment–Water Interface: Implications for Environmental Impact Assessment and Effects Monitoring
Bibliographic record
Abstract
Many contaminants in aquatic environments are associated with loosely packed aggregates of particulate material called flocs. Flocculation allows contaminants to accumulate at the sediment-water interface and it packages them in a form that is readily available for ingestion by filter feeding organisms. Unfortunately, most samplers being used for environmental assessment and monitoring suspend this material on impact and fail to sample this critical component of the seabed. In this study we use a slo-corer to collect seabed samples with an undisturbed surface layer and a Gust microcosm erosion chamber to erode the surface of the cores at increasing shear stresses. Results from two different sites, one impacted by tailings from historic gold mining and the other by open-pen salmon aquaculture, showed the levels of metals suspended at stresses below 0.24 Pa were greater than in the underlying sediment. Sampling this highly mobile surface layer is critical for determining the total contaminant load in bottom sediments and, more importantly, this layer represents the most readily available material for suspension. The loss of this layer during sampling could lead to inaccurate measurements of contaminant levels during environmental assessment and effects monitoring. A re-evaluation of the ISO standard for bottom sediment sampling is recommended.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".