Metal–Polycyclic Aromatic Hydrocarbon Mixture Toxicity in <i>Hyalella azteca</i>. 1. Response Surfaces and Isoboles To Measure Non-additive Mixture Toxicity and Ecological Risk
Bibliographic record
Abstract
Mixtures of metals and polycyclic aromatic hydrocarbons (PAHs) occur ubiquitously in aquatic environments, yet relatively little is known regarding their potential to produce non-additive toxicity (i.e., antagonism or potentiation). A review of the lethality of metal-PAH mixtures in aquatic biota revealed that more-than-additive lethality is as common as strictly additive effects. Approaches to ecological risk assessment do not consider non-additive toxicity of metal-PAH mixtures. Forty-eight-hour water-only binary mixture toxicity experiments were conducted to determine the additive toxic nature of mixtures of Cu, Cd, V, or Ni with phenanthrene (PHE) or phenanthrenequinone (PHQ) using the aquatic amphipod Hyalella azteca. In cases where more-than-additive toxicity was observed, we calculated the possible mortality rates at Canada's environmental water quality guideline concentrations. We used a three-dimensional response surface isobole model-based approach to compare the observed co-toxicity in juvenile amphipods to predicted outcomes based on concentration addition or effects addition mixtures models. More-than-additive lethality was observed for all Cu-PHE, Cu-PHQ, and several Cd-PHE, Cd-PHQ, and Ni-PHE mixtures. Our analysis predicts Cu-PHE, Cu-PHQ, Cd-PHE, and Cd-PHQ mixtures at the Canadian Water Quality Guideline concentrations would produce 7.5%, 3.7%, 4.4% and 1.4% mortality, respectively.
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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) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".