Application of a GIS-Based Modeling System for Effective Management of Petroleum-Contaminated Sites
Bibliographic record
Abstract
A GIS-aided simulation (GISSIM) system is developed for effective management of petroleum-contaminated sites in this study. The GISSIM contains two components: an advanced three-dimensional (3D) numerical model, and a geographical information system (GIS). The modeling component undertakes simulation for the fate of contaminants in subsurface unsaturated and saturated zones. The GIS component is used in three areas throughout the system development and implementation process: (1) managing spatial and nonspatial databases; (2) linking inputs, the model, and outputs; and (3) providing an interface between the GISSIM and its users. The system is applied to a North American case study. Concentrations of benzene, toluene, and xylenes in groundwater under a petroleum-contaminated site are dynamically simulated. Conditions of the contamination in different time stages under a variety of remediation scenarios are predicted. Reasonable outputs have been obtained and presented graphically. Implications of the modeling outputs have been analyzed based on the local environmental regulations. They provide quantitative and scientific bases for further assessment of site-contamination impacts and risks, as well as decisions of practical remediation actions. GISSIM is useful for both industrial and government sectors to make informed decisions on waste management, pollution control, site remediation, and environmental impact assessment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".