Presence of natural fracture as an indicator of operationaldifficulty for cave operations
Bibliographic record
Abstract
Cave mining methods are becoming the methods of choice due to their potential to extract ore from large low-grade deposits at depth at production rates comparable to open pit mines. However, the high upfront capital related to the degree of development and time lag before production require detailed planning and reliable studies to cope with the associated risks. One important geotechnical issue is managing rock mass fragmentation that affects both project value and safety. Both the scale of the problem and the lack of direct access to the rock mass at project evaluation and design stages make characterisation and accurate prediction of rock mass fragmentation a very difficult task. In this context, caving geomechanics is still largely an empirically based exercise. The discrete fracture network (DFN) approach uses fracture data collected from mapping of boreholes and rock exposures. The DFN approach was used to generate an in situ fragmentation model for the New Afton B1 Cave. The volumetric fracture intensity value (P32) is used as an indicator of the rock mass’s structural character, and provides a direct link to rock mass fragmentation. Major structures were included in the model and the spatial variability of the fracture intensity was analysed to derive a geostatistical model of rock mass fragmentation. The fragmentation ‘block model’ was then superimposed onto New Afton’s PCBCTM historical draw schedule model in an attempt to report blocks, representing the in situ fragmentation to drawpoint with respect to historical logs of hang-up event frequency, which also can refer back to height of draw (HOD). It was found that the influence of natural fractures diminishes gradually as the HOD increases. This is largely due to comminution in the draw column. The results showed that the model could identify areas of high hang-up events during initial draw prior to the column reaching maturity with respect to fragmentation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".