Understanding and managing surface subsidence at New Gold's New Afton block cave operation
Bibliographic record
Abstract
New Gold’s New Afton Mine is a 6 million tonne per year operating mine located 8 km outside of Kamloops, British Columbia, Canada. After ~17,700 m of decline access, underground ancillary and footprint development, the first drawbell was blasted in September 2011. The ore being mined is a copper–gold porphyry deposit situated within the Iron Mask batholith complex, bounded by two major subvertical fault structures, and plunges to the southwest. Determining the key components and driving mechanisms of surface subsidence is vital when considering potential impact on critical surface infrastructure. This paper will examine a number of fundamental learnings, starting from the initial feasibility study design assumptions through to actual cave behaviour and observed surface subsidence. As is the case in most start-up operations, early numerical modelling studies and inputs are often data poor and multiple assumptions need to be made. Furthermore, continuous and accurate calibration is required as subsidence evolves over time. This can be achieved through a better understanding of the regional geological model, and use of surface, subsurface and deep-seated instrumentation methods. At New Afton, we embarked upon an intensive instrumentation and drilling program to better understand the regional geological model and rock mass behaviour mechanisms, and their associated subsidence impacts.
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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) | 0.000 | 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".