Assessing toxicity of mining effluents: equilibrium- and kinetics-based metal speciation and algal bioassay
Bibliographic record
Abstract
Environmental context. The release of mining effluents exposes natural waters to excess metals and thereby threatens both human and environmental health. The present study explores the toxicity of aqueous mining effluents collected from a mining area in Sudbury (Ontario, Canada), using two different methods for determination of metal speciation, and an algal toxicity study. The results show reasonable correlation between metal speciation and the observed toxicity and suggest the importance of taking into account other factors related to water quality criteria such as nutrient concentrations, diluent water and presence of other toxic metals that can greatly influence the toxicological result. Abstract. The present study explores the toxicity of aqueous mining and municipal effluents from the Sudbury area (Canada) using equilibrium- and kinetics-based estimates of metal speciation and chronic toxicity studies using algae (Pseudokirchneriella subcapitata). Free metal ion concentration was determined by the Ion Exchange Technique (IET) and a computer speciation code, Windermere Humic Aqueous Model (WHAM) VI. Labile metal concentration was determined using the Competing Ligand Exchange Method. In general, no correlation was found between the observed IC25 (concentration of test substance that inhibits growth of organism by 25%) and the [Ni]labile, [Ni2+]IET or [Ni2+]WHAM, probably because of contributions by other metals such as Cu and Zn being also significant. Reasonable correlation (r2 = 0.7575) was found when the observed toxicity was compared with the sum of free metal ions of Cu, Ni, and Zn predicted by WHAM. The results of the present study reveal the importance of taking into account other factors related to water quality criteria such as nutrient concentrations, diluent water, and the presence of other toxic metals, which greatly influence the toxicological result in complex, multi-metal contaminated waters.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".