Rockburst prediction in kimberlite using decision tree with incomplete data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it