Research on the prediction of impact ground pressure hazard in deep coal mining based on moving average method
Bibliographic record
Abstract
As the mining depth of underground increases, the ground stress increases, which inevitably leads to an increase in the probability of impact ground pressure. The hidden danger of impact ground pressure seriously affects the safe and efficient mining of coal mines, so the early warning of impact ground pressure has an important role. In this paper, the identification and prediction of precursor characteristic signals of impact ground pressure are realized by moving average method, decision tree and support vector machine. The data are preprocessed by removing noise signals and normalization, extracting the "Class C" and "non-Class C" features of the preprocessed data, and adjusting the parameters to establish and optimize the interference signal recognition model based on the classification of the feature tree, and applying the model to identify the interference signal and determine the interference signal. The model is used to identify the interfering signals and determine the time interval of the interfering signals. Based on the feature tree classification algorithm of particle swarm optimization, the precursor feature signal identification model is established and applied to identify the precursor feature signals and determine their time intervals, and finally the feature tree algorithm is used to predict and analyze the probability of the appearance of precursor features.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".