Evaluating The Efficiency Of Various Reactive Media Removing Uranium From Groundwater
Bibliographic record
Abstract
Leachate seepage from uranium-contaminated tailings and sites from past uranium mining and milling activities remains a concern because it can contaminate surrounding groundwater, requiring assessment and remediation.Among the various clean-up techniques used to remediate these sites, Permeable Reactive Barriers (PRBs) stand out as a sustainable and cost-effective alternative for the remediation of contaminated groundwater.The objective of the present work is to evaluate the suitability of different reactive media for uranium removal as a first step for the deployment of a pilot-scale PRB in U-contaminated sites in Spain.For this purpose, several reactive materials were selected: activated carbon, Zero Valent Iron (ZVI), iron oxides, phosphates and clays.Batch equilibrium tests were conducted for 7 days at room temperature, using a concentration of 2g/l of reactive material and water with U concentration of 4560 ± 1000 μg/L, thus testing the behaviour of the materials under the physicochemical conditions of a contaminated medium.The adsorption capacity and removal efficiency of each reactive material were evaluated.Phosphates and activated carbon proved to be the most suitable options, both technically and economically.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".