A Case Study of Deep DNAPL Contamination in Marine Soft Clays
Bibliographic record
Abstract
Aquifers in China’s southeast coastal areas are protected by the overlying marine soft clays formed in Holocene transgression. However, a fundamental understanding of the characteristics of DNAPL (dense nonaqueous phase liquid) contamination in marine soft clays is limited. The study was conducted on the site of a former pharmaceutical factory in Shanghai, where serious 1,1,2-trichloroethane (DNAPL) contamination was detected up to the depth of 22.0 m below the existing grade. Partitioning calculation method was used to identify the presence of pure phase, and the results showed that pure 1,1,2-trichloroethane was accumulated at the interface of the upper soft silty clay and lower soft clay. The vertical transport was believed to be the pure DNAPL displacing pore water following the principle of two-phase flow, rather than the convection and diffusion of aqueous phase. The contamination (NAPL-soil interaction) impacted the soil properties slightly, and this effect could not account for the deep contamination. Soil structure analysis showed that these clays contained a proportion of large interaggregate pores, providing pathways for the transport of pure DNAPL. Considering their flocculated structure, the marine soft clays are not capable to perform as a barrier to prevent the downward migration of pure DNAPL.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".