Driving and managing stress in the Deep Mill Level Zone caving mine
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".