Assessment of the efficiencies of auxiliary ventilation systems using empirical methods
Bibliographic record
Abstract
Increasing depths and mechanization of underground mines have led to the production of large amounts of gaseous and particulate contaminants. Over 100 000 lives have been lost due to methane gas and dust explosions in coal mine workings in both the United States and Canada since 1900. There is, therefore, the need to constantly assess and evaluate the performance of existing mine ventilation systems to maintain safe and acceptable mine environmental conditions. This paper advances research initiatives in the control of methane gas in underground mine environments. It uses the results of continuous monitoring of methane gas concentrations conducted in selected coal mines in North America to assess the effectiveness of existing auxiliary ventilation systems to control methane gas concentrations. The results show that the average quantities of fresh air required to dilute, disperse and remove methane gas concentrations within set levels of one minute varied from 5.43 m 3 /sec. to 27.97 m 3 /sec. in the development headings. The average dilution times in the headings studied were less than eight minutes. The calculated dilution efficiencies of the auxiliary ventilation systems in the headings varied from 12% to 139%. These efficiencies ranged from poor to excellent. This implies that the auxiliary ventilation systems were capable of controlling the methane gas concentrations below statutory levels but may not be able to cope with large and unusual methane gas concentrations in the headings. This study is significant in the control of methane gas and coal dust explosions in coal mines.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".