Development of slag alternatives for paste backfill operations
Bibliographic record
Abstract
Paste backfilling, a critical step of the underground mining cycle which enables both increased resource recovery and provides a resilient tailings storage solution, is increasingly challenged by limited availability of industrial byproduct binders such as ground granulated blast furnace slag (GGBFS). The use of high quality GGBFS has proven to be critical for operators to address challenges posed by unique and complex ore compositions such as high sulphate ores while also contributing towards reducing the scope 3 greenhouse gas (GHG) emissions, especially as traditional cement binders can be responsible for up to 70% of the GHG emissions in the backfill process. This paper focuses on the development of a versatile engineered lime-based binder for paste backfill which maintains a low GHG footprint and can be made adaptable to various mine conditions, such as high sulphate ores, without compromising strength and other performance requirements. The novel binder has been tested in laboratory conditions by monitoring the unconfined compressive strength development over time using a sulphate-rich paste tailings provided by a commercial mining operation in Canada and has shown promising progress as a slag alternative.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".