Remediation of Petroleum-contaminated Sites through Simulation of a DPVE-aided Cleanup Process: Part 2. Remediation Design
Bibliographic record
Abstract
Remediation of petroleum-contaminated sites is usually a challenging task. It is hard to identify and customize a desired remediation technique or technique-combination into specific on-site conditions due mainly to the difficulties in gaining insight into the complex source and medium conditions in aquifer systems. Moreover, it is exigent to remediate sites where low-permeability soil layers exist. This study presents an integrated approach based on the simulation of a DPVE-aided (dual-phase vacuum extraction aided) remediation process for the identification and customization of desired remediation techniques, as well as its application to a site located in western Canada. Data of the specific site conditions, the forecasted results of contaminant transport, and the scenarios of remediation techniques with different treatment efficiencies are examined. Then the proposed approach was applied to design six remediation alternatives based on combinations of several technologies and the provision of analyses for system designs and costs. The study will help provide decision support for further remediation actions to be taken at the site.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".