Risk assessment of gas outburst in tunnels in non-coal formation based on the attribute mathematical theory
Bibliographic record
Abstract
Gas outburst can result in great loss of life and property during tunnel construction. Gas outburst in tunnels in non-coal formation is often more harmful than that in coal-bearing formation. In order to take effective countermeasures to prevent the occurrence of gas outburst during tunnel construction, it is essential to assess the risk of gas outburst before tunnel construction. This paper attempts to establish an assessment system for evaluating the risk of gas outburst in tunnels in non-coal formation in the survey phase of tunnelling based on the attribute mathematical theory. Based on the principle of relevance and operability, eight factors that influence the gas outburst in non-coal formation are selected as the attribute evaluation indices. Attribute measure functions are constructed to calculate the single index attribute measures for evaluation indices. The fuzzy Analytic Hierarchy Process is used to determine weights of evaluation indices. A confidence criterion is applied to recognize the risk grade of the evaluation object. The proposed attribute evaluation system is applied to assess the gas outburst risk in a tunnel in the survey phase. The evaluation results show good agreement with the practical situation of gas, verifying the applicability of this attribute assessment system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".