Complexation reduces nickel toxicity to purple sea urchin embryos (Strongylocentrotus purpuratus), a test of biotic ligand principles in seawater
Bibliographic record
Abstract
The potential for Ni toxicity in seawater is of concern because of mining and processing activities in coastal regions. Determining Ni speciation is vital to understanding and predicting Ni toxicity and for bioavailability-based nickel risk assessment. The goal of this study was to characterize the complexation of Ni in relation to toxicity using embryological development of purple sea urchin (S. purpuratus). It was predicted that free ion [Ni2+] would be a better predictor of toxicity than total dissolved Ni concentrations (NiD). Synthetic ligands with known logKf values (Ethylenediaminetetraacetic acid (EDTA), Nitrilotriacetic acid (NTA), tryptophan (TRP), glutamic acid (GA), histidine (HD), and citric acid (CA)) were used to test the assumptions of the biotic ligand model (BLM) for Ni in seawater. [NiD] was measured by graphite furnace atomic absorption spectroscopy (GFAAS) and Ni2+ was first quantified using the ion-exchange technique (IET) and then concentrations were measured by GFAAS; [Ni2+] was also estimated using aquatic geochemistry modelling software (Visual Minteq). The mean EC50 values for [NiD] in unmodified artificial seawater control was 3.6 µM (95% CI 3.0–4.5) [211 µg/L 95% CI 176–264] and the addition of ligands provided protection, up to 6.5-fold higher [NiD] EC50 for EDTA. Compared to the control, measured EC50 values based on total dissolved nickel were higher in the presence of ligands. As predicted by BLM theory, [Ni2+] was a better predictor of Ni toxicity with 17% variability in EDTA and CA media while there was 72% variability in the prediction of Ni toxicity with total dissolved Ni. The results of this research provide support for the application of BLM- based prediction models for estimating Ni impacts in seawater.
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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 | 0.001 |
| 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) | 0.001 | 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".