Low- and zero-cement frozen backfill within permafrost mining conditions: A review
Bibliographic record
Abstract
This review examines the emerging field of unconventional backfilling methods in freezing conditions, specifically tailored for remote mining operations in permafrost regions. Meeting the demand for mineral resources while addressing environmental concerns necessitates innovative approaches in mineral production and mining operations. This review aims to give a very first catalog of novel unconventional backfills in freezing conditions which is being encountered in remote mining areas like permafrost regions. As mining expands into these challenging environments, logistical obstacles, high costs, and environmental considerations arise. Conventional backfilling often relies on cement, which poses economic and environmental challenges due to carbon emissions and costs. Research has explored alternative backfill compositions and deployment strategies. Unconventional mixtures, sometimes without cement or using alternative binders, have gained attention in freezing conditions. Studies suggest that frozen backfill can be stronger than unfrozen alternatives, with water acting as a natural binder during freezing. However, the extended freezing process requires innovative deployment methods and consideration of seasonal limitations. Key findings indicate that faster freezing processes can improve strength, yet the adverse effects of freezing on cement hydration necessitate alternative materials, such as zero-cement backfill and using alternative binders. Despite the promise of unconventional backfilling, several areas still require further exploration. Mechanisms to enhance freezing rates, alternative binder materials, and factors affecting strength and thermal conductivity need continued investigation. In our pursuit of novel backfilling methods, there remains much ground to cover. The main outcome of this review is the identification and evaluation of unconventional backfill materials and methods for use in freezing conditions, highlighting their potential advantages over conventional cement-based backfills. It underscores the need for further research into alternative binders, deployment strategies, and the optimization of freezing processes to enhance backfill strength and address economic and environmental concerns in remote mining areas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".