Adjusting the influence function method for subsidence prediction
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. The extraction of ore and minerals by underground mining may induce ground subsidence phenomena. These phenomena produce several types of ground movement like horizontal and vertical displacements, ground curvature and horizontal ground strain at the surface, and associated building damage in urban regions. The influence function is a well-known and efficient method for the prediction of these movements, but its application is restricted to mining configurations with the same influence angle around the mine. However, this angle may display different values when the mine is not horizontal or when other subsidence events already occurred near the considered mine. In this paper a methodology and an algorithm are developed, based on the traditional influence function method in order to take into account different influence angles. This methodology is implemented in the Mathematica software and a case study is presented with data from the Lorraine iron mine field in France. Ground movements calculated with the developed methodology show a fair concordance with observed data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it