ArticleHardwareX2025
Programmable automated low-cost IoT water sampler.
Article in HardwareX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Unveiling biases in water sampling: A Bayesian approach for precision in edge-of-field monitoring.Journal of environmental qualityArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Water quality management is a critical environmental challenge for water resource managers in agriculture and other sectors due to pollution from contaminants like nitrogen and phosphorus. This pollution degrades ecosystems in waterways worldwide. Environmental pollutant mitigation methods rely heavily on the ability of managers to monitor water quality, often by collecting water samples (either by manual or automated methods) and sending them out for analyte characterization by a laboratory. Traditional automated samplers are often prohibitively expensive and/or complex, hindering effective water resource management across different contexts. Conversely, manual collection methods require more time and labor, but provide less data (i.e., a single point in time as opposed to a composite sample from multiple time points). Addressing this, the Colorado State University Agricultural Water Quality Program created a low-cost, automated water sampler (LCS) leveraging Internet of Things (IoT) technology that enables near-real-time, edge-of-field water monitoring. The LCS stands out for its affordability, simplicity, and real-time data provision, offering a practical tool for water resource managers seeking to monitor WQ. Furthermore, comparing LCS water quality and quantity data shows promising agreement, indicating that the device is a reasonable substitute for practical applications.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.