Preparation and characterization of nickel-spiked freshwater sediments for toxicity tests: Toward more environmentally realistic nickel partitioning
Bibliographic record
Abstract
Two spiking methods were compared and nickel (Ni) partitioning was evaluated during a series of toxicity tests with 8 different freshwater sediments having a range of physicochemical characteristics. A 2-step spiking approach with immediate pH adjustment by addition of NaOH at a 2:1 molar ratio to the spiked Ni was effective in producing consistent pH and other chemical characteristics across a range of Ni spiking levels. When Ni was spiked into sediment having a high acid-volatile sulfide and organic matter content, a total equilibration period of at least 10 wk was needed to stabilize Ni partitioning. However, highest spiking levels evidently exceeded sediment binding capacities; therefore, a 7-d equilibration in toxicity test chambers and 8 volume-additions/d of aerobic overlying water were used to avoid unrealistic Ni partitioning during toxicity testing. The 7-d pretest equilibration allowed excess spiked Ni and other ions from pH adjustment to diffuse from sediment porewater and promoted development of an environmentally relevant, 0.5- to 1-cm oxic/suboxic sediment layer in the test chambers. Among the 8 different spiked sediments, the logarithm of sediment/porewater distribution coefficient values (log Kd ) for Ni during the toxicity tests ranged from 3.5 to 4.5. These Kd values closely match the range of values reported for various field Ni-contaminated sediments, indicating that testing conditions with our spiked sediments were environmentally realistic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".