Statistic Analysis and Enlightenment on Major Accident in Coal Mine of China in 2011-2014
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
In order to research the general laws for great coal mine accidents of our country in recent years, the data of coal mine accidents were counted and analyzed by linking aspects such as accident types, occurring time, month, area and using the method of mathematical statistic. The analysis shows that the number of major serious coal mine accidents registered an annual decrease, of which the occurrence frequency of gas accidents was the highest from 2011 to 2014. The accident occurrence frequency in the second quarter was relatively high, but the average death toll was the lowest. The average death toll in the fourth quarter was the highest. Guizhou, Heilongjiang and Jilin are the provinces of high frequency of accidents. On this basis, this paper analyzes the causes of the accidents, proposed preventive measures, having practical significance to improve coal mine production safety.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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