Organochlorine Pesticides and Polychlorinated Biphenyl in Surface Sediments of the Dianchi Lake:The Distribution and Risk Assessment
Bibliographic record
Abstract
The Dianchi Lake is the largest lake in Yunnan Province and the sixth largest freshwater lake in China,with a reputation of a Pearl Imbedded on the Plateau.It plays a very important role in agriculture and the water supply of Kunming City.However,comprehensive studies on the environmental pollution of persistent organic pollutants in the Dianchi Lake have not been carried out in recent years.In this study,we collected eleven sediment samples from the Dianchi Lake to evaluate the contamination and ecological risks caused by OCPs and PCBs.The contamination pattern was DDTs HCHs PCBs.DDTs were the most abundant pollutants,their concentrations ranging from 0.26 to 75.20ng/g dry weight,followed by HCHs from0.63 to 26.0ng/g and PCBs from 0.64 to 17.07ng/g.The concentrations of DDTs,HCHs and PCBs decreased sharply,by 1or 2orders of magnitude,from the upper section to the lower section because of being obstructed by the dam.The PCB homologue profiles were dominated by less chlorinated compounds with tri-PCBs and penta-PCBs,which are related to the historical production and use of PCBs in China.The isomer ratios of(p,p'-DDE+p,p'-DDD)/DDTs(0.84)andβ-HCH/HCHs(0.45)suggested that the input of OCPs could be attributed to the heavy historical application which was preserved in agricultural soil in adjacent areas.Some sediment samples had higher concentrations of DDT compounds andγ-HCH than the standards from the Canadian Environmental Quality Guideline,suggesting the OCPs in the sediments of the upper Dianchi Lake might have posed a bit high harm to the aquatic environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".