Jericho open resistive data logger: An open-source modular weather station and monitoring system for long-term solar photovoltaic outdoor experimentation
Bibliographic record
Abstract
Environmental and energy production monitoring systems not only provide data acquisition (DAQ) but now supervisory control and data acquisition (SCADA) for both meteorological and solar photovoltaic (PV) research. DIY systems are often not robust enough for research and proprietary systems are often economically prohibitive. The Jericho Open Resistive Data Logger (RDL) platform bridges this gap between low-cost DIY devices and high-cost proprietary DAQs. It integrates a custom RDL, Arduino Nano, modular I 2 C expansion, and a Raspberry Pi for edge processing into a robust, open-source platform. Supporting multiple sensor protocols (analog, digital, resistive, I 2 C, SDI-12, and USB) and long-distance wired transmission, the system enables reproducible, research-grade data collection at less than half of the cost of proprietary stations. Statistical comparison of irradiance, relative humidity and temperature and wind speed were bench marked against a proprietary system and found to be well within acceptable differences for validation although wind speed was found to have the highest deviation. Two independent open-source units confirm excellent inter-device repeatability across all measured variables. By combining environmental and PV monitoring within a unified platform, Jericho Open RDL provides an accessible and adaptable solution for distributed renewable energy and environmental research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".