Low-cost air, noise, and light pollution measuring station with wireless communication and tinyML
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Different types of environmental pollution cause negative consequences to ecosystems throughout the globe, which humanity is now trying to mitigate. It is necessary to know the level of pollution problems in the immediate environment, to evaluate the impact of human activities, and mitigation strategies necessary to ensure habitability. For this reason, in this work, a low-cost pollution measurement station for outdoor or indoor use is proposed and developed that measures air pollution (particulate matter and CO2), noise (level and direction), light pollution (power and multispectral), and also relative humidity and ambient temperature. The system stores the data in an SD memory or transmits data in real-time to the internet via WiFi. The purposes of the system are to be used in environmental studies, to deploy monitoring networks, or to ensure the habitability of a living or working space. The prototype integrates the measurement of the different sources of contamination in a single compact device at USD$ 628.12 without sacrificing measurement accuracy. The system is validated for each variable with reference equipment, obtaining an average error of approximately 2.67% in the measurement of all the variables measured. The system is easy to assemble and has an option for power supply using solar photovoltaic devices and an alternative for connection to 2G/3G mobile networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it