A Simulation-Based Nonlinear Goal Programming Model for Groundwater Remediation Systems Design
Bibliographic record
Abstract
This study proposes an integrated method that simulates and optimizes groundwater design and management in combination with goal programming, which establishes the equilibrium between technical and environmental constraints in a pump-and-treat system.This method is applied to a petroleum-contaminated site in Western Canada to identify optimal remediation strategies given a set of remediation scenarios.The significant influential factors are remediation duration, standard concentration levels, and total pumping rate.Results indicate that goal programming can greatly enhance the remediation effect under low contaminant concentrations.In the pump-and-treat system, wells I2, E1, and E3 are the dominant components, whereas wells M7 and M5 are sensitive to variations in the identified influential factors.These wells must therefore be monitored intentionally.Moreover, these factors influence one another in interaction.Thus, high total pumping rates do not always generate favorable outcomes, and a long remediation period is unnecessary.In conclusion, the three identified factors should be spontaneously considered in the general goal-programming framework.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".