Modeling Preferential Flow in Reactive Barriers: Implications for Performance and Design
Bibliographic record
Abstract
Reactive barriers are passive and in situ ground water treatment systems. Heterogeneities in hydraulic conductivity (K) within the aquifer-reactive barrier system will result in higher flux rates, and reduced residence times, through portions of the barrier. These spatial variations in residence time will affect the treatment capacity of the barrier. A numerical flow model was used to evaluate the effects of spatial variations in K on preferential flow through barriers. The simulations indicate that the impact of heterogeneities in K will be a function of their location and distribution; the more localized the high K zone, the greater the preferential flow. The geometry of the reactive barrier will also strongly influence flow distribution. Aquifer heterogeneities will produce greater preferential flow in thinner barriers compared to thicker barriers. If the barrier K is heterogeneous, greater preferential flow will occur in thicker barriers. The K of the barrier will affect the flow distribution; decreasing the K of the barrier can result in more even distribution of flow. Results indicate that less variable flow will be attained utilizing thicker, homogeneous barriers. The addition of homogeneous zones to thinner barriers will be effective at redistributing flow only if installed immediately adjacent to both the up- and downgradient faces of the barrier.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".