Acute Toxicity of Ternary Cd–Cu–Ni and Cd–Ni–Zn Mixtures to <i>Daphnia magna</i>: Dominant Metal Pairs Change along a Concentration Gradient
Bibliographic record
Abstract
Multiple metals are usually present in surface waters, sometimes leading to toxicity that currently is difficult to predict due to potentially non-additive mixture toxicity. Previous toxicity tests with Daphnia magna exposed to binary mixtures of Ni combined with Cd, Cu, or Zn demonstrated that Ni and Zn strongly protect against Cd toxicity, but Cu-Ni toxicity is more than additive, and Ni-Zn toxicity is slightly less than additive. To consider multiple metal-metal interactions, we exposed D. magna neonates to Cd, Cu, Ni, or Zn alone and in ternary Cd-Cu-Ni and Cd-Ni-Zn combinations in standard 48 h lethality tests. In these ternary mixtures, two metals were held constant, while the third metal was varied through a series that ranged from nonlethal to lethal concentrations. In Cd-Cu-Ni mixtures, the toxicity was less than additive, additive, or more than additive, depending on the concentration (or ion activity) of the varied metal and the additivity model (concentration-addition or independent-action) used to predict toxicity. In Cd-Ni-Zn mixtures, the toxicity was less than additive or approximately additive, depending on the concentration (or ion activity) of the varied metal but independent of the additivity model. These results demonstrate that complex interactions of potentially competing toxicity-controlling mechanisms can occur in ternary-metal mixtures but might be predicted by mechanistic bioavailability-based toxicity models.
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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) | 0.001 | 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 | 0.000 | 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".