Bibliographic record
Abstract
A measurement and development platform for collecting water quality data (the WaterWatcher) was developed. The platform includes sensors to measure turbidity, total dissolved solids (TDS), and water temperature as variables that are often collected to assess water quality. The design is extensible for research and monitoring purposes, and all design files are provided under open-source permissive licenses for further development. System design and operation are discussed for illustrative purposes. A block diagram indicates elements of mechanical, electrical, and software design for this system. The mechanical assembly used to house circuit boards and sensors is designed using 3D printing for rapid prototyping. The electronic circuit board acts as a carrier for an Arduino 32-bit microcontroller board and an associated cellular module along with a GPS for geolocation of water quality measurements. The cellular module permits data transfer for Internet of Things (IoT) functionality. System operation is set up using a command line interface (CLI) and C++ code that allows for calibration coefficients and human-readable transfer functions to be defined so that sensor voltages are related to physical quantities. Data are cached on a secure digital (SD) card for backup. The circuit was calibrated, and system operation assessed by deployment on an urban reservoir. Biogeochemical cycles were identified in the collected data using spectrogram and semivariogram analyses to validate system operation. As a system with hardware and software released under an open source license, the WaterWatcher platform reduces the time and effort required to build and deploy low-cost water quality measurement sensors and provides an example of the basic hardware design that can be used for measurements of water quality.<br>Dr. Nicholas Kinar & Dr. Markus Brinkmann<br>@ University of SaskatchewanSaskatoon, Saskachewan, Canada
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 | 0.003 | 0.011 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.689 | 0.094 |
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; both teacher heads agree on what is shown here.
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".