Chemical Speciation and Toxicity of Nickel Species in Natural Waters from the Sudbury Area (Canada)
Bibliographic record
Abstract
Metal complexation properties of dissolved organic carbon (DOC) in freshwaters are recognized but poorly understood. Here, we investigated the release of free nickel from Ni-DOC complexes using nickel-polluted freshwaters from Sudbury (Canada). We used the Competing Ligand Exchange Method with Chelex-100 as the competing ligand to measure the rate of free Ni2+ ion released by the dissociation of Ni-DOC complexes. The kinetic studies showed that the fastest kinetically distinguishable component representing approximately 30-95% of the total nickel had a dissociation rate coefficient similar to that reported for [Ni(H20)6]2+. High concentrations of Ca2+ and Mg2+ caused a larger amount of the DOC-bound nickel to be released as free Ni2+ ion. Growth inhibition of the freshwater alga Pseudokirchneriella subcapitata was highly correlated with the Ni/DOC ratio, the free plus labile nickel concentration, and the dissociation rate coefficient. While the levels of metals were not sufficient to kill Daphnia magna, these test organisms were immobilized in the same samples that showed algal growth inhibition. Only one sample caused 22% death of Hydra attenuata. The algal toxicity tests were consistent with the kinetic speciation results and are consistent with the hypothesis that dissolved [Ni(H20)6]2+ plus other labile nickel species are toxic forms of Ni present.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".