Reactive Sandpacks for In-Situ Treatment of Construction Dewatering Effluent
Bibliographic record
Abstract
Dealing with contaminated effluent can be problematic when dewatering is required in the course of removing buried tanks, excavating contaminated soils or constructing groundwater remediation systems. An engineering solution put forward in this paper is the emplacement of a sandpack, around the dewatering well(s), that reacts with and sequesters groundwater contaminants of concern. If an appropriate granular reactive material is available, placing that material around the dewatering well screen can achieve a degree of contaminant treatment in-situ that may considerably reduce the costs of storing, securing and treating the water before it is released. In the preferred situation, the contaminants remain underground and the extracted water is suitable for surface release as it is pumped from the ground. We used laboratory columns of granular reactive material (clinoptilolite) and a radiostrontium tracer to evaluate the usefulness of this approach in a hypothetical situation where the water table needed to be lowered 2 m in order to install a permeable reactive barrier. Results were consistent with the concept of reducing the environmental liabilities of construction dewatering in areas of groundwater contamination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".