Case study: Holistic test work campaigns for tailings management system designs
Bibliographic record
Abstract
A gold mine in East Africa has decided to implement a new paste backfill system.To implement the overall project while ensuring that all the design, environmental and government requirements are satisfied, the mine decided to undertake a comprehensive test work campaign to understand their tailings.The test work program focused on the following:Transport of tailings to the Tailings Storage Facility (TSF) and 2. Tailings used for paste backfill material.For the design of the transportation system of tailings to the TSF, the test program focused on the basic material and slurry properties which were used as inputs for the various other tests.Large-scale pipe loop tests were conducted on the tailings to measure the existing system's pressure gradients, stationary deposition velocities and centrifugal pump performances at the operating density range (30%m to 50%m).For the paste backfill system design, the test program focused on thickening and filtration tests to determine the dewatering characteristics of the tailings.Filter cake bulk material handling tests were conducted to provide input to conveyor and chute designs.In order to understand the material that would be placed underground, unconfined compressive strength and rheology tests were conducted as part of the paste backfill reticulation design, and modified acid-base accounting, net acid generation and shake flask, and tank leach tests were conducted on the cemented paste mixes as part of the environmental tests.This paper presents the laboratory test results on which the overall tailings management design was based.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".