Driving and managing stress in the Deep Mill Level Zone caving mine
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
The Deep Mill Level Zone (DMLZ) panel cave mine in PT Freeport Indonesia continues to ramp up production since initial undercutting began in 2015. At approximately 1,500 m below surface, the DMLZ is one of the world’s deepest caving operations. The current undercut area has reached 60,000 m2, within a 1,200 m triangular-shaped perimeter. This deep mining environment has given rise to substantial pre-mining and induced loading conditions. Stress management in production areas represents the toughest challenge to date for the operation. In addition to applying lessons learned from the overlying Intermediate Ore Zone (IOZ) and Deep Ore Zone (DOZ) mines, extensive empirical ground response data has been collected from undercut and extraction levels. The understanding of how the various geological, geotechnical and mine design criteria interact has resulted in a much-improved approach towards DMLZ stress management. The most notable successes related to DMLZ stress management include improved understanding of ground response, accelerated cave growth, and increased production rates. This paper summarizes the key learnings regarding stress management in production areas and outlines positive improvements undertaken towards sustained, safe caving in the DMLZ.
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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 | 0.000 | 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 | 0.000 |
| 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