Acute toxicity of binary and ternary mixtures of Cd, Cu, and Zn to <i>Daphnia magna</i>
Bibliographic record
Abstract
Standard static-exposure acute lethality tests were conducted with Daphnia magna neonates exposed to binary or ternary mixtures of Cd, Cu, and Zn in moderately hard reconstituted water that contained 3 mg dissolved organic carbon/L added as Suwannee River fulvic acid. These experiments were conducted to test for additive toxicity (i.e., the response to the mixture can be predicted by combining the responses obtained in single-metal toxicity tests) or nonadditive toxicity (i.e., the response is less than or greater than additive). Based on total metal concentrations (>90% dissolved) the toxicity of the tested metal mixtures could be categorized into all 3 possible additivity categories: less-than-additive toxicity (e.g., Cd-Zn and Cd-Cu-Zn mixtures and Cd-Cu mixtures when Cu was titrated into Cd-containing waters), additive toxicity (e.g., some Cu-Zn mixtures), or more-than-additive toxicity (some Cu-Zn mixtures and Cd-Cu mixtures when Cd was titrated into Cu-containing waters). Exposing the organisms to a range of sublethal to supralethal concentrations of the titrated metal was especially helpful in identifying nonadditive interactions. Geochemical processes (e.g., metal-metal competition for binding to dissolved organic matter and/or the biotic ligand, and possibly supersaturation of exposure waters with the metals in some high-concentration exposures) can explain much of the observed metal-metal interactions. Therefore, bioavailability models that incorporate those geochemical (and possibly some physiological) processes might be able to predict metal mixture toxicity accurately.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".