Injury Prediction for Canadian Mineral Exploration Using Machine Learning
Bibliographic record
Abstract
The mineral exploration industry is a vital contributor to the Canadian economy, yet it remains among the most hazardous sectors due to the complex and risky nature of mining operations. The primary objective of this research is to comprehend and predict the specific nature of injury severity within Canada’s mineral exploration field to enhance existing occupational health and safety measures. The proposed research is distinctive as it utilizes data from the entire mineral exploration industry in Canada, gathered by the Prospectors and Developers Association of Canada (PDAC). Advanced machine learning (ML) techniques are employed to construct a framework capable of predicting and monitoring injuries in four different classes. Following a description of the dataset’s distribution, eight distinct machine learning methods, including Support Vector Machine, Convolutional Neural Network, Bayesian Neural Network (BNN), logistic regression, decision trees, random forest, Gradient Boosting, and Long Short-Term Memory (RNN LSTM), were applied to predict the nature of injury in different mining activities. The results of the framework indicated that multi-classification with RNN-LSTM outperformed other algorithms, accurately identifying the degree of injury with 97% accuracy across all metrics. These findings have the potential to significantly contribute to injury prevention efforts by increasing awareness of potential safety risks and providing quantitative predictions of fatal injuries and future accidents in mining exploration fields.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".