One year of paste operations at Jabal Sayid, Saudi Arabia
Bibliographic record
Abstract
It took five years to bring mining with paste backfill to Ma’aden Barrick Copper Company’s (MBCC) Jabal Sayid Mine in Saudi Arabia. The work involved various studies, multiple test programs, site visits for benchmarking and detailed engineering before the paste system was commissioned in October 2017. Barrick is a world leader in paste backfill and drew on international teams to conceptualise, design and construct this 225 m3/hr cemented paste backfill system. Value engineering, peer reviews and risk management workshops were held throughout the process to ensure MBCC received value for money and a reliable system. The paste plant was required to handle a tailings stream that was originally planned to produce hydraulic fill (the coarse fraction) but through the reintroduction of fine tailings was able to generate a good paste product that met mining needs. Challenges involved getting the most out of the tailings dewatering circuits (both fine and coarse streams), the local conditions (temperatures >50°C), large bulk stopes fed by a gravity system and the capital cost associated with building a high throughput system with significant cement storage. This paper presents the history of the project, test work, engineering design and construction, commissioning, and training required to fill the first stope. More recent backfill monitoring, data logging, improvements and ongoing optimisation of the system that have continued through the first year of paste production are also presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".