Effect of major cations (Ca2+, Mg2+, Na+, K+) and anions (SO 42−, Cl−, NO 3−) on Ni accumulation and toxicity in aquatic plant (<i>Lemna minor</i> L.): Implications For Ni risk assessment
Bibliographic record
Abstract
The effect of major cation activity (Ca(2+) , Mg(2+) , Na(+) , K(+) ) on Ni toxicity, with dose expressed as exposure (total dissolved Ni concentration NiTot ) or free Ni ion activity (in solution Ni(2+) ), or as tissue residue (Ni concentration in plant tissue NiTiss ) to the aquatic plant Lemna minor L. was examined. In addition, Ni accumulation kinetics was explored to provide mechanistic insight into current approaches of toxicity modeling, such as the tissue residue approach and the biotic ligand model (BLM), and the implications for plant Ni risk assessment. Major cations did not inhibit Ni accumulation via competitive inhibition as expected by the BLM framework. For example, Ca(2+) and Mg(2+) (sulfate as counter-anion) had an anticompetitive effect on Ni accumulation, suggesting that Ca or Mg forms a ternary complex with Ni-biotic ligand. The counter-anion of the added Ca (sulfate, chloride, or nitrate) affected plant response (percentage of root growth inhibition) to Ni. Generally, sulfate and chloride influenced plant response while nitrate did not, even when compared within the same range of Ca(2+) , which suggests that the anion dominated the observed plant response. Overall, although an effect of major cations on Ni toxicity to L. minor L. was observed at a physiological level, Ni(2+) or NiTot alone modeled plant response, generally within a span of twofold, over a wide range of water chemistry. Thus, consideration of major cation competition for improving Ni toxicity predictions in risk assessment for aquatic plants may not be necessary.
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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) | 0.001 | 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 | 0.001 | 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".