Ecological Risk of Nonylphenol in China Surface Waters Based on Reproductive Fitness
Bibliographic record
Abstract
It has been recognized that ecological risk assessment based on traditional endpoints of toxicity are unable to provide adequate protection because some chemicals may affect reproductive fitness of aquatic organisms at much lower concentrations. In this paper, predicted no effect concentrations (PNECs) for 4-nonylphenol (NP) were derived based either on endpoints of survival, development, and growth or on some nonlethal biomarkers of reproduction, biochemical and molecular biology data. The PNECs derived from reproductive lesion ranged from 0.12 to 0.60 μg NP L(-1), which was significantly lower than those derived from other endpoints. An assessment of ecological risks posed by NP to aquatic organisms in surface waters of China was conducted based on concentration levels of NP in 16 surface waters of 4 major river basins and PNECs derived from reproductive fitness by a tiered ecological risk assessment (ERA). The results showed that 14.2% and 76.5% of surface waters in China may have ecological risks resulting from reproductive fitness if the thresholds of protection for aquatic organisms were set up as 5% (HC5) and 1% (HC1), respectively. The risks were significantly greatest in the Yangtze River Basin than in other major river basins. In comparison with the risks assessed based on traditional endpoints, such as lethality, for those chemicals causing adverse effects on reproduction due to modulation of endocrine function, to be protective of ecosystem structure and function, lesser PNECs, based on sublethal effects of reproduction, were appropriate.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".