A study on OCPs distribution,sources and risk in the sediment from Guanhe Estuary
Bibliographic record
Abstract
In this study,OCPs( Organochlorine Pesticides) in surface and column sediments from Guanhe Estuary( taken in April,2011) were analyzed by GC-ECD and210 Pb methods. The results showed that OCPs concentrations in surface sediments ranged from nd( Not Detected) to 58. 3 × 10- 9( dry weight),and the concentrations range among three sampled regions are,from highest to the lowest,intertidal zone,estuary area and Guanhe River. However,concentrations of O,P'-DDT were highest in the sediment from estuary area,compare to those from interidal zone or Guanhe River. Based on the studies in other areas in China,DDTs and HCHs concentrations around Guanhe estuary were average. OCPs concentrations in column sediments ranged from 2. 0 × 10- 9to 850. 0 × 10- 9,with an average of 210. 0 ×10- 9,and the results also indicated that the OCPs were relatively low in 1990 s and increased in 2000. These results were validated by the studies around the area,that there were new inputs of DDTs sources in this region during the study period. Meanwhile,the ecological risks were also evaluated according to Sediment Quality Guidelines( SQCs)applied in Canada. According to SQCs,the adverse effects of OCPs in this area mainly originated from contaminationsof DDD and DDT,which could occur frequently. The study suggested that more research on risk assessment of OCPs need to be conducted in this area,and feasible solutions to restore sediment environments need to be formulated for Guanhe estuary.
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 | 0.001 |
| Science and technology studies | 0.001 | 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".