Health-Risk-Based Groundwater Remediation System Optimization through Clusterwise Linear Regression
Bibliographic record
Abstract
This study develops a health-risk-based groundwater management (HRGM) model. The model incorporates the considerations of environmental quality and human health risks into a general framework. To solve the model, a proxy-based optimization approach is proposed, where a semiparametric statistical method (i.e., clusterwise linear regression) is used to create a set of rapid-response and easy-to-use proxy modules for capturing the relations between remediation policies and the resulting human health risks. Through replacing the simulation and health risk assessment modules with the proxy ones, many orders of magnitude of computational cost can be saved. The model solutions reveal that (i) a long remediation period corresponds to a low total pumping rate, (ii) a stringent risk standard implies a high total pumping rate, and (iii) the human health risk associated with benzene would be significantly reduced if it is regarded as constraints of the model. These implications would assist decision makers in understanding the effects of remediation duration and human-health risk level on optimal remediation policies and in designing a robust groundwater remediation system. Results from postoptimization simulation show that the carcinogenic risk would decrease to satisfy the regulated risk standard under the given remediation policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".