Use of shotcrete to control acid mine drainage in waste rock piles
Bibliographic record
Abstract
A research program supported by Westmin Resources and the Mineral Development Agreement (MDA), has evaluated the use of cementitious dry surface covers in the prevention of water and oxygen infiltration into acid waste rock piles. This paper presents the results of a field trial of a dry cover over a large test area on a waste rock pile at the Westmin Myra Falls site. The field trial was supported by Westmin Resources and CANMET under the Mine Environment Neutral Drainage (MEND) program. The objectives of this study were to apply the cementitious cover in the most cost effective manner and to evaluate the material properties and the long term efficacy of the cover system. Approximately 3500 square metres of area was covered using the shotcrete process. A robotic arm mounted onto a vehicle was used to apply the shotcrete onto the rock slope. The cementitious material used in the project incorporated high volumes of fly ash (a waste product) to reduce the material cost. The shotcrete mix also included the use of polypropylene fibres to control cracks. The test area was instrumented with infiltration boxes and survey markers to monitor seepage through the shotcrete and settlement in the rock slope. The performance of this test area will be monitored over the subsequent years to evaluate the long term durability of this cover when subjected to field conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".