Tunnel liner yield forecasting at Cigar Lake Mine: An input variable selection approach to understanding machine learning processes
Bibliographic record
Abstract
It is often time prohibitive for geotechnical professionals to examine large datasets to investigate complex rock mass phenomena in detail. Machine learning algorithms (MLAs) are gaining momentum for data processing in rock engineering, however research into their practical applications are just emerging. These multivariate rock mass datasets are ideal for developing MLAs to forecast rock mass behaviour. An Input Variable Selection (IVS) approach is presented for a Convolutional Neural Network (CNN) that predicts tunnel liner yield due to squeezing ground conditions at the Cigar Lake Mine. A IVS method called Input Omission (IO) is modified and applied to the CNN to enhance its performance. The IO method ranks the CNN inputs in terms of usefulness for forecasting the output. The IO findings for this CNN indicate that none of the available inputs may be omitted, and that the geotechnical zones and radial tunnel displacement inputs contain the strongest signals for forecasting the severity of tunnel liner yield at Cigar Lake Mine.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 teacher head, 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".