Identifying cause in sediment assessments: bioavailability and the Sediment Quality Triad
Bibliographic record
Abstract
Application of the Sediment Quality Triad approach to non-pH-stressed Sudbury area lakes clearly demonstrated increased metal (Cd, Co, Cu, and Ni) contamination, decreased abundances of some benthic invertebrates (amphipods, pisidiid clams, and tanytarsiid midges), and severe sediment toxicity to amphipods and mayflies. However, the bioavailability of metals and the cause of biological effects cannot be identified from these data. Bioaccumulation measurements, however, clearly demonstrated increased Cd, Co, and Ni bioavailability to Hyalella. Copper concentrations in Hyalella were not elevated, in spite of much higher sediment Cu concentrations. Nickel was the only metal accumulated in sufficient amounts to cause toxicity. Toxicity in Hyalella caged above the sediments was equivalent to that of animals exposed directly in sediment, demonstrating that effects were due to dissolved metal and not metals in the solid phase. Copper concentrations in overlying water were much lower than Ni concentrations and insufficient to account for toxicity. Including bioaccumulation data with traditional measurements of sediment chemistry, benthic community composition, and sediment toxicity allows quantification of metal bioavailability and identification of the cause of toxicity. This approach results in a much more complete assessment of the biological impacts of metals in sediments.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".