Enhanced Stabilization of Dikes and Levees Using Direct Current Technology
Bibliographic record
Abstract
A demonstration study conducted between late July and early October, 2006, at the Erie Pier Confined Disposal Facility (CDF) in Duluth, MN, suggests that direct current technology can simultaneously dewater and retard water movement through a leaking dike. Four electrode (anode and cathode) configurations/combinations were tested between late July and early October, 2006, but the most significant effects took place within the first 14 days of operation, when measured dike leakage dropped by more than 70 percent and dike settlement/consolidation reached 50 percent of its final value. The results indicate that direct current technology can be an effective method for reducing water flow through a dike and physically stabilizing a dike structure via electrokinetic dewatering and through the in-situ electrolytic introduction of aluminum to the dike soil using aluminum anodes. Other indicators of the technology's impact include: changing piezometer levels over time; visible movement of water to both the horizontal and vertical cathodes; and significant electrochemical deterioration of the aluminum-donating anodes. It is recommended that these technologies be further applied and evaluated at "real world" sites where dewatering and consolidation of saturated soils and sediments is needed, accompanied by more rigorous and quantitative monitoring and measurement of project variables.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".