Study of Characteristics of Surface Subsidence in Longwall Coal Mine under Poor Ground Conditions in Indonesia
Bibliographic record
Abstract
This paper aims to study the characteristics of surface subsidence induced by longwall mining under poor ground conditions in Indonesia by means of numerical simulation techniques using finite difference code “FLAC3D”. The effect of mining depth in cases of single panel and multi-panel longwall mining, the influence of panel and pillar widths, and the impact of backfilling material, were incorporated into the FLAC3D software. The simulated results indicated that the angle of draw and maximum surface subsidence were significantly associated with the depth of mining, the number of extracted panels, the width of panel and pillar, and the type of backfill. In single panel mining, the largest maximum surface subsidence is observed in case of the shallowest mining depth, and it gradually decreases as the depth increases. In contrast, the angle of draw increases with increasing the mining depth. In multi-panel mining, the angle of draw and maximum surface subsidence increase as the mining depth increases. Moreover, the angle of draw and maximum surface subsidence decrease when the narrow panel and large pillar widths are adopted, and the backfilling materials are applied.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".