Open-source three-dimensional IoT anemometer for indoor air quality monitoring
Bibliographic record
Abstract
Ventilation in an enclosed space can significantly influence people's comfort, health, and safety. Poor ventilation can generate temperatures dangerous to health or obstruct the dispersion of environmental pollutants, such as toxic gases or pollution. Measuring indoor environmental conditions can thus help improve the quality of the environment and protect people's health and comfort. This work proposes the design of an open-source anemometer to measure wind speed and direction in three dimensions. The purpose of this anemometer is to monitor wind conditions in enclosed spaces and environmental conditions related to air quality and temperature. The prototype uses an array of six unidirectional flow sensors, each pointing towards a different axis. Carbon dioxide (CO 2 ), volatile organic compounds (VOC), temperature, humidity, pressure, and gas presence sensors are integrated to monitor indoor environmental conditions accurately. Measuring the vertical component of the wind provides more detailed information on wind conditions. Test results show that the device can detect variations in wind speed with a deviation of 0.25 m/s, detect changes in horizontal wind direction with a deviation of 3.7°, and detect vertical wind direction variations with a deviation of 3.02°. These measurements demonstrate that the proposed device is capable of detecting wind changes in three dimensions, validating its potential for detailed indoor airflow monitoring.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".