Importance of calcium in modifying the acute toxicity of sodium sulphate to<i>Hyalella azteca</i>and<i>Daphnia magna</i>
Bibliographic record
Abstract
Modification of the acute toxicity of sodium sulphate to Hyalella azteca and Daphnia magna was investigated using exposure water with different levels of water hardness (expressed as CaCO3 equivalents) and calcium-magnesium molar (Ca:Mg) ratios. The influence of Ca:Mg ratios on the toxicity of sodium and potassium chloride to D. magna also was investigated. For both species, the mean lethal concentrations that resulted in mortality of 50% of the sample population (LC50s), expressed as mg SO4(2-)/L, were increased significantly in harder water and in water with higher Ca:Mg ratios. The LC50s for H. azteca increased from 569 to 5259 mg/L with a change in water hardness from 25 to 250 mg/L. Furthermore, modifying the Ca:Mg ratio from 0.7 to 7.0 at a constant hardness of 100 mg/L significantly increased LC50s from 2101 to 2725 mg/L. The LC50s for D. magna were also significantly higher in harder water with LC50s increasing from 1194 to 3203 mg/L with a change in water hardness from 25 to 100 mg/L. In addition, modifying the Ca:Mg ratio from 0.7 to 7.0 significantly increased LC50s from 1194 to 1985 at a constant hardness of 25 mg/L, and from 3203 to 4395 mg/L at a constant hardness of 100 mg/L. No significant change in the toxicity of potassium or sodium chloride to D. magna was observed in waters with higher Ca:Mg ratios.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".