Nickel Partitioning and Toxicity in Sediment during Aging: Variation in Toxicity Related to Stability of Metal Partitioning
Bibliographic record
Abstract
Metals in sediment can be complexed by minerals, partition between solid and aqueous phases, and cause toxicity at high concentrations. We studied how the oxidation of surface sediment that occurs during aging alters the partitioning and toxicity of Ni. Two sediments (Burntwood and Raisin) were amended with Ni, equilibrated, incubated in a flow-through flume, and examined for sediment physicochemistry and toxicity to Hyalella azteca (7 day growth). Through time, the sediment surface (5 mm) was oxidized, acid-volatile sulfide concentrations declined in Raisin sediment, and amorphous Fe oxides increased. Porewater Ni concentrations declined through time but total Ni concentrations in sediment were unchanged, suggesting changes in Ni partitioning through time. Both sediments elicited a toxic dose–response by H. azteca early in the aging process; but only Burntwood, for which Ni was primarily partitioned to Fe oxide minerals, exhibited a consistent dose–response during aging. Low total Ni concentrations (20 mg kg –1 ) in Raisin sediment reduced H. azteca growth at initiation, but all Ni treatments (up to 3000 mg kg –1 ) exhibited similar growth after 12 days of aging. The dynamic toxicity observed in Raisin sediment was likely due to the instability of NiS in surface sediments early in the aging process. These data suggest that short-term toxicity assays with homogenized Ni-amended sediment (i.e., standard sediment toxicity tests) may be accurate for sediments where Ni speciation is dominated by oxidized ligands; however, under high-AVS and high-Fe conditions, calculated toxicity thresholds may be overly conservative (here by >100-fold) with respect to natural sediment conditions.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".