Predict Metal Toxicity and Water Quality Criteria of Different Types of Water Into Taihu Lake, China Using Biotic Ligand Model
Bibliographic record
Abstract
The Biotic Ligand Model (BLM) is an important tool in predicting acute toxicity of metal to aquatic organisms. This study used the BLM to predict acute toxicity (LC50, the lethal concentration for 50% of test organisms) of Cu and Zn to Daphnia magna, and to predict water quality criteria (WQC) for different types of water (river water, treated and untreated sewage, and rainwater) into Taihu Lake, an important drinking water source for millions of population. Their effects of metal toxicity on Taihu Lake were also explored. The results showed that Taihu Lake water had a relatively higher LC50 (Cu) value (1.38 ± 0.60 mg/L) than other types of water except for untreated sewage (2.25 ± 2.13 mg/L). Of these types of water, predicted LC50 value of Cu followed the order of untreated sewage > rainwater > Taihu Lake > river water > sewage effluent. In addition, acute toxicity of Zn followed the order of river water > rainwater > Taihu Lake > sewage effluent > untreated sewage. Comparison of the predicted WQC values with the Chinese National Water Quality Standard of Surface waters indicated that the existing standard for copper might over-protect the aquatic organisms in Taihu Lake. The correlation analysis showed that increased DOC (dissolved organic carbon) concentration resulted in a higher LC50 value due to complexation between metal and organic matters, which reduced Cu toxicity. A significant positive relationship existed between cation concentration and predicted LC50 value, also indicating that cation concentration may reduce metaltoxicity. The work provided basic data for the study of water effect ratio (WER) and for the establishment of China’s own water quality critieria based on Chinese water environmental conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".