Adjusting the Influence Function Method for Subsidence Prediction
Bibliographic record
Abstract
Theextraction of ore and minerals by underground mining may induce groundsubsidence phenomena. These phenomena produce several types of ground movement likehorizontal and vertical displacements, ground curvature and horizontal groundstrain at the surface, and associated building damage in urban regions. Theinfluence function is a well-known and efficient method for the prediction ofthese movements, but its application is restricted to mining configurationswith the same influence angle around the mine. However, this angle may displaydifferent values when the mine is not horizontal or when other subsidenceevents already occurred near the considered mine.In this paper a methodology and analgorithm are developed, based on the traditional influence function method inorder to take into account different influence angles. This methodology isimplemented in the Mathematica software and a case study is presented with data from the Lorraine iron minefield in France. Ground movements calculated with the developed methodologyshow a fair concordance with observed data.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".