Remediation of Salt Impacted Groundwater with Electrokinetics.
Bibliographic record
Abstract
This paper describes how Alberta Transportation is supporting leading research in the use of Electrokinetics in order to enhance the reclamation of salt impacted groundwater. Electrokinetic technology has long been used in applications that require soil dewatering and more recently there has been widespread research and application of this technology into contaminated sites treatment. Electrokinetic remediation is achieved by applying an electric field over the impacted area causing the movement of ions. Pilot studies and research demonstrate that the total voltage will vary from site to site, as it is dependent on the conductivity of the soil and the spacing of the anode and cathode. It is most applicable in low permeability soils, where extraction is difficult, because the soil is typically saturated and is not readily drained. The process has also been used, or is being investigated, for remediation of heavy metals, radionuclides, and organic contaminants. This new innovative research is being conducted at an Alberta Transportation salt storage yard where the impact of salt has caused considerable impact to surrounding vegetation. The pilot study will be in operation for two years to investigate the feasibility of In-situ Electrokinetics combined with vapour extraction and pneumatic fracturing; to enhance the removal of salts from salt impacted soil and groundwater. The paper presents a discussion of the project, including a summary of preliminary results, and economics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".