Residues of DDTs and their spatial distribution characteristics in soils from the Yangtze River Delta, China
Bibliographic record
Abstract
Organochlorine pesticides were used extensively in the Yangtze River Delta, China. However, knowledge about their residual levels and environmental fates in soils of this area is limited. This paper presents the residue isomers and spatial pattern of dichlorodiphenyltrichloroethane (DDT) in soils across 17 main cities in the Yangtze River Delta. Forty-three soil surface (0-15 cm) samples were collected during a field campaign conducted in October 2003 in the Delta. Six DDT isomers (1-[2-chlorophenyl]-1-[4-chlorophenyl]-2,2-dichloroethane [o,p'-DDD], 1-[2-chlorophenyl]-1-[4-chlorophenyl]-2,2-dichloroethylene [o,p-'DDE], 1,1,1-trichloro-2-[p-chlorophenyl]-2-[o-chlorophenyl]ethane [o,p'-DDT], p,p'-dichlorodiphenyldichloroethane [p,p'-DDD], p,p'-dichlorodiphenyldichloroethylene [p,p'-DDE], p,p'-dichlorodiphenyltrichloroethane [p,p'-DDT]) were detected using gas chromatography. The results show that p,p'-DDE was the dominant isomer in the soil samples. The levels of DDT are generally low in soils of this area and are comparable to DDT levels in other cities in China and in soils from developed countries such as the United States and Germany. The isomer ratios of o,p'-DDT to p,p'-DDT and DDT to (DDD + DDE) were employed to identify the source of DDT. The computed ratios implied that the source of DDT might be related to the application of dicofol, an acaricide manufactured from technical DDTs and mainly used on cotton fields to treat mites.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".