Bibliographic record
Abstract
A Convolutional Neural Network (CNN) was developed for Cigar Lake Mine, Saskatchewan, Canada, to predict tunnel liner yield. The mine experiences a complex time-dependent ground squeezing behaviour resulting from the poor geological conditions and the artificial ground freezing implemented to stabilize the ore cavities and to control ground water during the ore extraction process. Four inputs were used inn the CNN to make this prediction: geotechnical zone mapping, primary support class, ground freezing pattern, and measured tunnel displacement. A sensitivity analysis of the CNN training parameters, called hyperparameters, was completed to optimize the final CNN performance. Hyperparameters analyzed include: the amount of training data, the convolutional filter size, and the error weighting scheme. Two final models were developed, one balanced model able to accurately predict tunnel liner yield across all classes of severity, and one targeted model that is calibrated to predict the higher classes of tunnel liner yield particularly well. Input Variable Selection (IVS) was applied to examine how the CNN used the given data, or inputs, to forecast rock mass behaviour. The three IVS methods investigated were Channel Activation Strength (CAS), Input Omission (IO), and Partial Correlation (PC). The IO and PC approaches proposed are novel for CNNs using a spatial and temporal geomechanical dataset.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".