A Low-Cost, Open-Source, 3D-Printed, Compact, In Situ, Automatic Water Sampler for Environmental Surveillance
Bibliographic record
Abstract
Water sampling is crucial for assessing and managing urban water systems, and wastewater sampling has expanded applications for public health surveillance. This paper introduces an innovative, open-source, compact autosampler, “ MAD A uto- S ampler (MAD-AS)”, to overcome the significant cost, space, and installation limitations of conventional water automatic samplers. MAD-AS can collect samples from diverse water sources. The device integrates a 3D-printed peristaltic pump, protective housing, and an ATmega-based microcontroller for user-defined sampling programs. It can be installed in space-constrained areas, sits in situ within the waterway, and can be remotely triggered via cellular connectivity. Laboratory tests validated MAD-AS’s consistent performance, both regarding its pumping rate and its ability to sample complex water matrices with high pollutant variability. Field deployments ( n = 75) in stormwater and wastewater systems demonstrated comparable performance to traditional sampling methods, particularly for smaller or dissolved pollutants (TP, TN, viruses) with significant correlations ( p < 0.05). MAD-AS’s low-cost, easy installation with small batteries for power supply, and accessible design aim to enhance our temporal and spatial understanding of water quality variations across diverse catchments, including in remote and informal communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".