A Complete Mobile Treatment Chain to Produce Drinking Water from Sources Heavily Contaminated by Inorganic and Organic Compounds
Bibliographic record
Abstract
The provision of potable water for armed forces at their operational sites necessitates a robust treatment chain to ensure the production of safe drinking water from potentially contaminated local water sources. Relying on single-use water bottles is not considered an eco-friendly option and on-site production may exhibit limited efficiency depending on the water contamination. This study therefore aimed to define a mobile processing chain that could efficiently produce drinking water on-site while offering a multi-barrier level of protection. To evaluate the system, contaminated water was prepared from different water sources and then spiked with various inorganic contaminants (metals, anions: Cl−, F−, I−, NO2−, NO3−, SO42−, CN−), organic contaminants (e.g., pesticides, petroleum hydrocarbons, polycyclic aromatic hydrocarbons, chlorinated solvents), and energetic compound (perchlorate) at levels ranging from 5 to 50 times the standard water quality criteria. A specific treatment process was defined optimized and evaluated at flow rates reaching 500 L/h. This treatment chain includes the following: a sediment filter, a greensand filter, a cation exchange resin, an anion exchange resin, an activated carbon adsorption filter, ultrafiltration, a UV lamp, and a reverse osmosis (RO) unit. This treatment system successfully met all water quality criteria, providing a reliable and effective alternative to an RO-only treatment regime.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".