An often-overestimated ecological risk of copper in Chinese surface water: bioavailable fraction determined by multiple linear regression of water quality parameters
Bibliographic record
Abstract
Abstract Background Risks of adverse ecological effects of copper (Cu) consider of water quality parameters were not fully understood in China. Here, a national-scale exposure of Cu in Chinese surface water was investigated, and the first report using multiple linear regression approach to predict and correct toxicity data based on water chemistries in China. Risk of Cu was overestimated without considering water quality parameters in the previous studies. Results Under prevalent water quality conditions of hardness = 150.0 mg/L, pH = 7.8, and dissolved organic carbon (DOC) = 3.0 mg/L, across China, the predicted no effect concentration for total, dissolved Cu was 9.71 μg/L. Based on results of the preliminary risk quotients method, 1.19% (a total of 43 in 3610 sites) were classified as “high risk”, only one sixth of the percentage of sites with “high risk” than the proportion predicted when not considering water quality parameters, which was 7.51%. Similar results were obtained by application of both the margin of safety method (0.71% compared to 2.81%) and joint probability curve method (3.34% compared to 16.29%), both of which overestimated risks posed by Cu to aquatic organisms in China. Conclusion After correcting for bioavailability based on water quality parameters, consider both concentrations and frequencies during ecological risk assessment, regions of China at greatest risk from adverse effects of Cu were the Hai River ( Haihe ), Huai Rivers ( Huaihe ) and Chao Lake. These findings provide a comprehensive method for a more accurate assessment of risks of adverse effects of Cu to aquatic life in surface waters.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".