Pollution characterization and source apportionment of HCH and DDT in sewage irrigation soil of Xiao Qing River wetland
Bibliographic record
Abstract
To investigate the residues and pollution sources of DDT and HCH in the Xiao Qing River sewage irrigated wetland and study the distribution of pollutants(DDT and HCH) along the vertical soil profile,soil samples were pretreated by Soxhlet extraction and silica gel purification,and the HCH and DDT content were analyzed by GC-MS.The results show that the concentrations of ΣHCHs and ΣDDTs in the soils range from ND-0.225 μg/kg and ND-1.204 μg/kg respectively,with the mean concentrations of 0.042 μg/kg and 0.204 μg/kg.The concentrations of DDTs and HCHs are below the national soil environmental quality standards(GB 15618—1995),which are at a low residue levels.The HCH residues in wetlands without irrigation are caused mainly by historical pesticide using and there is few pollutions generated recently.DDT contamination of wetlands exists mainly in the form of DDE,that is due to historical pesticide using.Sewage irrigation reduced soil residues of DDT and HCH,with average reduction rate of 67.16% and 78%.The HCH residues are higher than DDT residues at the same point.The DDT contents drop sharply along the soil profiles,and the HCH contents change irregularly along the soil profiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".