Evaluating the acute toxicity of binary and ternary mixtures of Cu, Zn, As and Se to Daphnia magna: a component-based approach using isobologram analyses and ternary diagrams
Bibliographic record
Abstract
The acute toxicity of binary and ternary combinations of Cu, Zn, As and Se to Daphnia magna was investigated. The aim was to provide a detailed characterization of the interactive effects of these elements in relation to aquatic toxicity. The binary and ternary combinations were evaluated using isobolograms, the concentration addition (CA) model, the independent action (IA) model, and the Hewlett model. They were also implemented using the MixModel package in R. This approach facilitated the identification of potential synergism, additivity, and antagonism, in addition to the characterization of binary and ternary interactions within a mixture composition space. For the binary combinations, Cu − Zn and Zn − Se had the most robust evidence for synergism with EC 50 estimates <1 toxicity unit (TU), as low as 0.27 TU for Cu − Zn , and 0.30 TU for Zn − Se . Combinations of As − Cu , As − Se , Cu − Se , and As − Zn were mostly additive, with EC 50 estimates ∼1 TU for all evaluated ratios. In all of the tested ternary combinations, Cu was identified the most toxic component. Emergent effects, which were defined as toxicity responses arising from combinations of toxicity which were not dependent on binary combinations, were observed in some ternary combinations. Synergism was observed in the As − Cu − Se and As − Cu − Zn mixtures, suggesting that binary combinations alone are insufficient for predicting interactions involving more than two components. Ternary diagrams also showed potential areas of high toxicity ( TU > 3 ), mostly in the Cu-dominated mixtures. These observations suggest that the toxicity assessment of trace metals and metalloids commonly found in mining and metallurgical effluents (in this case Cu, Zn, As, and Se) must take into account the effect of mixtures and not rely on substance-by-substance assessments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".