Characterizing toxicity of metal-contaminated sediments from the Upper Columbia River, Washington, USA, to benthic invertebrates
Bibliographic record
Abstract
Sediments from the Upper Columbia River, Washington, USA, are contaminated with metals from smelting operations. We conducted short-term and long-term tests with the midge Chironomus dilutus and the amphipod Hyalella azteca and short-term tests with the freshwater mussel Lampsilis siliquoidea with 54 sediments from the Upper Columbia River to characterize thresholds for toxicity of metals to benthic invertebrates. Test sediments were screened for toxicity by comparisons with low-metal reference sediments. Toxic effects on amphipods occurred primarily in sediments from the upstream (riverine) reach, and toxic effects on midges occurred in sediments from both the upstream reach and the downstream (reservoir) reach. Little toxicity was observed in mussel tests. Toxicity thresholds (20% effect concentrations [EC20s]) for metals in sediment and porewater were estimated from logistic concentration-response models. Copper (Cu) concentrations in the simultaneously extracted metal fraction of sediments and bioavailable Cu in porewater, as characterized by biotic ligand models, had consistent associations with toxicity endpoints. Concentration-response models for sediment Cu produced EC20s for 6 endpoints, with long-term amphipod survival and reproduction being the most sensitive. A logistic regression model fitted to an endpoint sensitivity distribution for sediment Cu predicted that approximately one-half of the sediments tested would be toxic to at least one endpoint and that approximately 20% of test sediments would be toxic to more than half of the endpoints. These results indicate that sediments from the upstream reach of the Upper Columbia River, which contain high concentrations of metals associated with slags, cause a wide range of toxic effects in laboratory tests and are likely to have adverse effects on benthic invertebrate communities. Environ Toxicol Chem 2018;37:3102-3114. Published 2018 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".