A case study on the commingling of tailings and waste rock at a Brownfields open cast mine in Ghana
Bibliographic record
Abstract
Tailings management is multifaceted in its ultimate goal of zero harm to people and the environment, in line with the Global Industry Standard on Tailings Management (GISTM).Gold Fields is committed to conforming with the GISTM and achieving its environment, social and governance (ESG) priorities, of which one is investigating the feasibility of tailings innovation projects, such as the tailings moisture content reduction.There is significant mining industry interest in developing geo-stable tailings storage facilities, combining tailings and mine waste to form a geochemically and physically stable landform.However, while geo-stable materials are a topic of industry-wide interest, there is a lack of a sound knowledge base and testing protocols to assess, compare and validate the performance of different technical approaches across different mineralogical and operational situations.The paper presents the characterisation of material used to construct geo-stable tailings and waste rock management facilities and the performance of such facilities at the trial pad scale.The ultimate aim in the commingling journey at this site is to form single-footprint waste management systems with a reduced total footprint area compared to constructing separate tailings storage facilities and waste rock dumps.The material characteristics, operational environment, trial pad construction, placement and testing methods will be discussed as part of this initial step in the geo-waste journey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".