Assessment of mixture toxicity of copper, cadmium, and phenanthrenequinone to the marine bacterium <i>Vibrio fischeri</i>
Bibliographic record
Abstract
Transition metals and polycyclic aromatic hydrocarbons (PAHs) are cocontaminants at many sites. Contaminants in mixtures are known to interact with biological systems in ways that can greatly alter the toxicity of individual compounds. The toxicities (individually and as mixtures) of copper (Cu), a redox-active metal; cadmium (Cd), a nonredox active metal; and phenanthrenequinone (PHQ), a redox-active oxygenated PAH, were examined using the bioluminescent bacterium Vibrio fischeri. We found that the cotoxicity of Cu/PHQ was dependent on the ratio of concentrations of each chemical in the mixture. Different interaction types (synergism, antagonism, and additivity) were observed with different combinations of these toxicants. The interaction types changed from antagonism at a low Cu to PHQ ratio (1:4), to additive at an intermediate Cu to PHQ ratio (2:3), to synergistic at higher Cu to PHQ ratios (3:2 and 4:1). In contrast to Cu/PHQ mixtures, the cotoxicity of Cd/PHQ did not change at different mixture ratios and was found for the most part to be additive. For the individual chemicals and their mixtures, reactive oxygen species (ROS) production was observed in V. fischeri, suggesting that individual and mixture toxicity of Cu, Cd, and PHQ to V. fischeri involves ROS-related mechanisms. This study shows that mixture ratios can alter individual chemical toxicity, and should be taken into account in risk assessment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".