Evaluation of Viscosity, Strength and Microstructural Properties of Cemented Tailings Backfill
Bibliographic record
Abstract
In this study, the particle size distribution and chemical composition of gold mine tailings were examined experimentally. A series of viscosity and uniaxial compressive strength (UCS) tests were used to study the relations between the viscosity of cemented tailings backfill (CTB) slurry, the solid content (SD), and the cement-to-tailings ratio (c/t). Relations between UCS performance of CTB and SD, c/t, and curing time (CT) were discussed while examining the microstructure of 28-day cured backfill with different solid contents. Results illustrate that a major increase in CTB viscosity by increasing the SD leads to the formation of tailings grains for a skeleton formation, which is formed due to consolidation and gravitational forces. The CTB’s strength increases with the increase of c/t, SD, and CT, due to a decrease in water-to-cement ratio and porosity, and an increase in hydration products over time. The SEM micrographs show how CTB’s microstructure is affected by the SD, generating ettringites and calcium silicate hydrates in the backfill matrix. The findings of this study will lead to an efficient CTB mix design for reaching the higher performance in underground mining structures, thereby reducing expenses related to the backfill.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".