Design and Implementation of a Low-cost IoT Smart Weather Station Framework
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
Monitoring systems have become more and more crucial in our lives in recent years. Consequently, we propose in this work an automatic weather monitoring system that is capable of obtaining dynamic, real-time climatic data for a given area. The Internet of Things (IoT), a sophisticated and effective method of connecting objects to the Internet, serves as the scientific foundation for this system. Many areas of practice and science rely on accurate climate measurements. Location and time of day influence weather factors, especially in tropical areas where there are no seasons. Furthermore, as precision agriculture or smart agriculture advances, it will be crucial to improve the measurement of systems that are widely distributed over growing areas. For these reasons, the design, building, and fabrication of a mobile air conditioner with Wi-Fi connectivity and solar energy power is presented in this work. This station measures both relative humidity and temperature. Additionally, workstations can be configured and managed remotely. The program's objective is to promote the creation of freely accessible, open-source hardware. A meteorological base station framework based on the ESP-32 Internet of Things device development board is proposed in this study. The system has a feature that records the ambient temperature and humidity. The ESP-32 web server then makes the data available to the user. A web page is also part of the system to make controlling adjacent instruments easier. The system gets its power from solar electricity.
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 | 0.002 |
| 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