Study of methane emissions occurred in an underground coal mine located in the north of Spain using outburst risk indices, Fuzzy logic and mathematical models
Bibliographic record
Abstract
Two major methane emissions have been recorded in the underground coal mine of Hullera Vasco Leonesa (province of Leon, North of Spain).These were 3,750 m 3 in May 2009, with seven workers seriously affected, and 31,250 m 3 in October 2013, which resulted in six deaths because of asphyxia.Based on the main factors that contribute to outbursts (geological factors, coal strength, stress conditions, gas content, and desorption characteristics), estimated according to tests in the mine, a fuzzy logic model has been developed that allows us to calculate the probability of a future outburst.Simultaneously, two mathematical models have been developed: the first for studying the evolution of methane emitted in the initial burst and the second to study the process that causes the methane to spread through the roadway.The values obtained from the two models have been validated with measurements obtained with the methanometers and oxymeters installed in the sublevel caving and roadways and with the measurements collected by rescue team members equipped with self-contained breathing apparatuses and hand-held methane and oxygen detectors.From these studies, the most likely cause of methane emissions and its evolution in roadways have also been determined.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".