Orthogonal array analysis of overburden failure due to mining of multiple coal seams
Bibliographic record
Abstract
This paper presents a numerical analysis that uses an orthogonal array to investigate overburden failure caused by longwall mining of multiple coal seams.An LN(s k ) array is said to be an orthogonal array with s levels, N rows, and k columns.This analysis identifies the contrasting factors that influence the height of the caving and water-conducting fractured zones.The factors include the mechanical properties of the interburden layers (factor A), thickness of the interburden layers (factor B), and mining height of the lower seam (factor C).The mechanical properties of the overburden failure are quantitatively investigated and the correlations among the factors are evaluated.The results show that factors A, B, and C all have a significant influence on the interactions during mining of multiple seams.This means that mining of the lower seam significantly changes the height of the caving zone of the entire system, with factors B and C having the most influence.Factor B has a more obvious effect on the waterconducting fractured zone than factor A, but factor C has the greatest effect among the factors.An equation to describe a dividing line, which is set to denote the relationship between the non-interaction ratio (K) and the cutting height of the lower seam (M) is proposed.This modified dividing line can be used to determine whether interactions exist among the overburden failure zones.
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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 | 0.002 |
| 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) | 0.001 | 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".