Developing a Hydrologic Monitoring Network in Data‐Scarce Regions Using Open‐Source Arduino Dataloggers
Bibliographic record
Abstract
Core Ideas An innovative low‐cost open‐source Arduino‐based datalogger was developed. The datalogger was deployed for hydrologic monitoring in tropical watersheds. Arduino datalogger performance was robust after overcoming initial challenges. The system has great potential for automated continuous environmental monitoring. Continuous hydrologic monitoring is limited in many regions of the world, creating serious knowledge gaps for water resources managers and scientists. Recent advances in open‐source software and hardware technologies, such as the Arduino project, show potential for the development of low‐cost (∼$100) automated dataloggers required for continuous data collection. We developed an Arduino‐based datalogger (the Ecohydro Logger) coupled with water sensors providing digital output to establish a hydrologic monitoring network in the data‐scarce wet‐dry tropics of Guanacaste, Costa Rica. While we experienced some challenges with a first iteration of our Arduino‐based datalogger, an improved version was robust and able to capture long periods of high‐frequency stream discharge data. Integration of the monitoring program into the local community was also key to successful deployment, allowing exchange of local knowledge and support. The accessible and low‐cost nature of Arduino‐based dataloggers can provide a means to extend continuous environmental monitoring into data‐scarce regions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".