Bioaccumulation of waterborne Ni in<i>Dreissena polymorpha</i>: A stable isotope experiment to assess the effect of zinc, calcium, and dissolved organic matter
Bibliographic record
Abstract
The effect of Ca, Zn, and dissolved natural organic matter (NOM) on waterborne Ni accumulation was investigated in a freshwater mussel. An enriched stable metal isotope tracer was required to measure the Ni uptake rate accurately. Zebra mussels were exposed to environmentally relevant concentrations of (62) Ni (from 0.5 to 8 µg/L) for 48 h in media spiked with Ca, Zn, or dissolved NOM. The (62)Ni uptake was inhibited by Ca (from 0.138 ± 0.021 to 0.061 ± 0.010 L/g/d for Ca concentrations ranging from 43 to 133 mg/L) and enhanced by Zn (from 0.051 ± 0.006 to 0.109 ± 0.007 L/g/d for Zn concentrations ranging from 6.6 to 38.3 µg/L). The mechanisms behind the synergistic effect of Zn remain unclear, yet it can be hypothesized that Ni uptake is facilitated by Zn-dependent transport sites. To formalize the effects of Ca and Zn, a model was proposed to express the Ni uptake rate as a function of the mussels' filtration rate and of Ca and Zn concentrations. The (62)Ni uptake increased at low NOM concentrations and decreased at higher concentrations. This could be explained by the influence of NOM on both the speciation of Ni and the filtration activity of mussels. At high NOM concentrations, a modification of the membrane's permeability might also have favored Ni uptake, although this was not clearly established in this study. Therefore, the effect of water composition on Ni bioavailability to zebra mussels cannot be predicted by competition and complexation models alone, because it also influences the animal's physiology.
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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".