Integrated Subsurface Modeling and Risk Assessment of Petroleum-Contaminated Sites in Western Canada
Bibliographic record
Abstract
Soil and groundwater contamination can pose a variety of impacts and risks to communities. Identification of management schemes with sound environmental and socio-economic efficiencies is desired. Before any decisions regarding site remediation actions can be made, three major questions may have to be answered. They are namely “What happened underground?”, “What will happen in the future under the given remediation scenarios?”, and “Are there specific risks to the surrounding community?”. In this study, an integrated modeling and risk assessment method is developed for effectively managing petroleum-contaminated sites through technically answering the above questions. It presents an integral concept that integrates issues of multicontaminant transport simulation, biodegradation modeling, health risk assessment, and site remediation for real-world problems within a general decision support framework. The developed method is applied to a petroleum-contaminated groundwater system in western Canada for identifying cost-effective management schemes with improved environmental and socio-economic efficiencies. The research outputs are directly useful for the decision maker to gain insight into the site and to make remediation decisions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".