New Approach for Monitoring the Underground Coal Mines Atmosphere Using IoT Technology
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
Because the atmosphere in underground coal mines contains toxic and flammable gasses, assessing the well-being of miners at all times while working in underground coal mines is an important task.The hazardous environment in underground coal mines reduces the miners' performance, which negatively affects the overall productivity of the mines.Therefore, it is necessary to regularly monitor the environment of underground mines so that appropriate safety measures can be taken.In this work, an IoT-based system was proposed using sensors to detect the concentration of mine gasses, air temperature, and humidity in the environment of underground mines.The developed wireless monitoring system was tested under laboratory conditions for measuring carbon dioxide, carbon monoxide, methane gas, air temperature, and humidity.The proposed monitoring system allows to store the measurement data that will help in predicting future hazardous conditions through artificial neural network and machine learning.The results of this research will help to introduce an innovative monitoring technology in underground coal mines so that miners' safety can be improved by changing safety measures from preventive to predictive.
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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