Improving sediment-quality guidelines for nickel: Development and application of predictive bioavailability models to assess chronic toxicity of nickel in freshwater sediments
Bibliographic record
Abstract
Within the framework of European Union chemical legislations an extensive data set on the chronic toxicity of sediment nickel has been generated. In the initial phase of testing, tests were conducted with 8 taxa of benthic invertebrates in 2 nickel-spiked sediments, including 1 reasonable worst-case sediment with low concentrations of acid-volatile sulfide (AVS) and total organic carbon. The following species were tested: amphipods (Hyalella azteca, Gammarus pseudolimnaeus), mayflies (Hexagenia sp.), oligochaetes (Tubifex tubifex, Lumbriculus variegatus), mussels (Lampsilis siliquoidea), and midges (Chironomus dilutus, Chironomus riparius). In the second phase, tests were conducted with the most sensitive species in 6 additional spiked sediments, thus generating chronic toxicity data for a total of 8 nickel-spiked sediments. A species sensitivity distribution was elaborated based on 10% effective concentrations yielding a threshold value of 94 mg Ni/kg dry weight under reasonable worst-case conditions. Data from all sediments were used to model predictive bioavailability relationships between chronic toxicity thresholds (20% effective concentrations) and AVS and Fe, and these models were used to derive site-specific sediment-quality criteria. Normalization of toxicity values reduced the intersediment variability in toxicity values significantly for the amphipod species Hyalella azteca and G. pseudolimnaeus, but these relationships were less clearly defined for the mayfly Hexagenia sp. Application of the models to prevailing local conditions resulted in threshold values ranging from 126 mg to 281 mg Ni/kg dry weight, based on the AVS model, and 143 mg to 265 mg Ni/kg dry weight, based on the Fe model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".