Rockburst prediction in kimberlite using decision tree with incomplete data
Bibliographic record
Abstract
A rockburst is a common engineering geological hazard. In order to predict rockburst potential in kimberlite atan underground diamond mine, a decision tree method was employed. Based on two fundamental premises ofrockburst occurrence,σσσW,,,θct ETare determined as indicators of rockburst, which are also partition at-tributes of the decision tree. 132 training samples (with 24 incomplete samples) were obtained from realrockburst cases from all over the world to build the decision tree. The decision tree based on 108 completesamples was built with an accuracy of 73% for 15 validation samples while another decision tree based on 132samples (with 24 groups of incomplete data) shows an accuracy of 93% for validation samples. Hence, thesecond decision tree was employed for kimberlite burst prediction. 12 samples from lab tests and a numericalmodel were used as test samples. The results indicate a moderate burst liability which matches real situations atthe diamond mind in question.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".