Metal–Polycyclic Aromatic Hydrocarbon Mixture Toxicity in <i>Hyalella azteca</i>. 2. Metal Accumulation and Oxidative Stress as Interactive Co-toxic Mechanisms
Bibliographic record
Abstract
Mixtures of metals and polycyclic aromatic hydrocarbons (PAHs) are commonly found in aquatic environments. Emerging reports have identified that more-than-additive mortality is common in metal-PAH mixtures. Individual aspects of PAH toxicity suggest they may alter the accumulation of metals and enhance metal-derived reactive oxygen species (ROS). Redox-active metals (e.g., Cu and Ni) are also capable of enhancing the redox cycling of PAHs. Accordingly, we explored the mutual effects redox-active metals and PAHs have on oxidative stress, and the potential for PAHs to alter the accumulation and/or homeostasis of metals in juvenile Hyalella azteca. Amphipods were exposed to binary mixtures of Cu, Cd, Ni, or V, with either phenanthrene (PHE) or phenanthrenequinone (PHQ). Mixture of Cu with either PAH produced striking more-than-additive mortality, whereas all other mixtures amounted to strictly additive mortality following 18-h exposures. We found no evidence to suggest that interactive effects on ROS production were involved in the more-than-additive mortality of Cu-PHE and Cu-PHQ mixtures. However, PHQ increased the tissue concentration of Cu in juvenile H. azteca, providing a potential mechanism for the observed more-than-additive mortality.
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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) | 0.001 | 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".