[Ecological risk assessment of organophosphorus pesticides in aquatic ecosystems of Pearl River Estuary].
Bibliographic record
Abstract
The risk quotient method and a probabilistic risk assessment method were applied for assessing aquatic ecological risk of nine organophosphorus pesticides, including thimet, dichlorovos, disulfoton, dimethoate, dimethyl parathion, chlorpyrifos, ethoprophos, sumithion and malathion on eight aquatic organisms in the Pearl River Estuary. Results using the risk quotient method revealed that the risk level of opossum shrimp was the highest among eight aquatic organisms of the Pearl River Estuary. The risk of water flea and midge was in medium level, followed by the rest six aquatic organisms, including diatom, oyster, carp, catfish and eel, which were in the low risk by the examined organophosphorus pesticides. It was found that thimet made the largest contribution to total aquatic ecological risk among nine organophosphorus pesticides to every organism. The results from probabilistic risk assessment showed that the total ecological risk in high water period was higher than that in low water period determined by the HC5 under the 95% confidence level. The largest contribution of thimet to total aquatic ecological risk subject to the HC5 in 50% confidence level was regarded as the toxic reference value. The probabilistic risk of a single contaminant showed that thimet and disulfoton were harmful to exceeded 10% organisms in the estuarine. The probabilistic risk of nine pesticides mixture in high water period was also higher than that in low water period, and both risks were greater than 5% which exceeded safety threshold for 95% organisms in the Pearl River Estuary.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".