Open-source Arduino-compatible data loggers designed for field research
Bibliographic record
Abstract
Abstract. Automated electronic data loggers revolutionized environmental monitoring by enabling reliable high-frequency measurements. However, the potential to monitor the complex environmental interactions involved in global change has not been fully realized due to the high cost and lack of modularity of commercially available data loggers. Responding to this need, we developed the ALog (Arduino logger) series of three open-source data loggers, based on the popular and easy-to-program Arduino microcontroller platform. ALog data loggers are low cost, lightweight, and low power; they function between −30 and +60 ∘C, can be powered by readily available alkaline batteries, and can store up to 32 GB of data locally. They are compatible with standard environmental sensors, and the ALog firmware library may be expanded to add additional sensor support. The ALog has measured parameters linked to weather, streamflow, and glacier melt during deployments of days to years at field sites in the USA, Canada, Argentina, and Ecuador. The result of this work is a robust and field-tested open-source data logger that is the direct descendant of dozens of individuals' contributions to the growing open-source electronics movement.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".