A versatile model for the evaluation of subsidence hazards above underground extractions
Bibliographic record
Abstract
All underground extraction – oil, gas, water and minerals – results in subsidence of the surface to some degree. Subsidence can cause damage to infrastructure – roads, powerlines, gas and oil pipelines, buildings – and to the natural surface, with the development of cracking, potholes, changes in hydrogeology and destabilization of slopes. Pre-extraction estimates of the amount of subsidence and the hazards it might produce are difficult to determine with accuracy, and the most frequent approach is to model the surface movements in response to extraction using empirically based models. There are a number of large underground coal mine projects on the drawing board in British Columbia and Alberta despite the current prolonged episode of reduced coal prices. Fortunately, almost all of these projects target metallurgical coal, for which windmills, hydro and nuclear “clean” power sources provide no substitute and in fact, on which they depend for their construction. Each of these projects will have to demonstrate satisfactory mitigation of hazards arising from potential subsidence before they will be allowed to proceed. DMT Geosciences Ltd of Calgary, AB has recently worked with an underground mine proponent to model subsidence over an entire mine layout, in native coordinates and for multiple seam extraction, using a proprietary influence function model. Currently calibrated using a best estimate of western coal subsidence characteristics, the model itself will undergo additional calibration as monitoring data above the actual mine is obtained. The model itself is fairly easy to use, quick to run and provides results in an easily managed format for graphical display. As well as mining subsidence, it has in the past been shown to predict surface movements due to oil and water extraction at depth. For the current project. the results obtained in the initial subsidence prediction phase have allowed areas of potentially hazardous or damaging surface movements to be determined.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".