Copper and nickel partitioning with nanoscale goethite under variable aquatic conditions
Bibliographic record
Abstract
Metal contaminated sediments can be toxic to aquatic organisms and are common in human-dominated ecosystems, which results in metals being a leading cause of ecosystem impairment. Bioavailability of metals is influenced by their affinity for dissolved and solid-phase ligands, including iron (Fe) oxyhydroxides, which have been hypothesized to reduce metal toxicity in sediments. The authors examined the adsorption kinetics of copper (Cu) and nickel (Ni) with goethite (α-FeOOH) and characterized the influences of solute metal concentration, pH, ionic strength, and humate concentration on steady-state partitioning of the metals with goethite under conditions representative of natural aquatic environments. Copper and Ni readily adsorbed to goethite, and steady-state partitioning was achieved within 2 h. Although ionic strength had no effect on metal partitioning, adsorption of Cu and Ni to goethite was enhanced by alkaline pH and reduced by competition with humate. Because distribution coefficient (KD ) values for Cu and Ni from the present study are comparable to values measured in natural systems, the authors hypothesize that goethite may contribute significantly to the adsorption of both Ni and Cu to particles in the environment. The authors suggest that incorporating binding by Fe oxides in metal bioavailability models should be a priority for improving risk assessment of metal-contaminated oxic sediments.
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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) | 0.000 | 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".