A Low-Cost, Open-Source, 3D-Printed, Compact, In Situ, Automatic Water Sampler for Environmental Surveillance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it