Mechanical and microstructural characteristics of cemented paste tailings modified with nano-calcium carbonate and cured under various thermal conditions
Bibliographic record
Abstract
This paper focuses on the evaluation of the strength development and microstructure of nano-calcium carbonate (CaCO3) cemented paste backfill experimentally cured under isothermal conditions at room temperature and non-isothermal conditions in the field. A series of mechanical (uniaxial compressive strength, UCS) and microstructural (thermogravimetric, mercury intrusion porosimetry, scanning electron microscope) tests as well as monitoring experiments are experimentally conducted on cemented paste backfill (CPB) specimens with and without nano-calcium carbonate and cured at different times and under isothermal or non-isothermal conditions. The results show that the addition of nano-CaCO3 particles to CPB significantly improves its mechanical strength, irrespective of the thermal curing conditions (isothermal, field non-isothermal conditions). However, the impact of nano-CaCO3 particles on the increase in strength of CPB is only effective or significant at the early ages (curing time≤7 days). It is also found that the higher temperatures improve the accelerating effect of nano-CaCO3 much more than they accelerate the PCI hydration reactions in the first 3 days. Moreover, it is also found the sulphate ions present in the natural gold tailings negatively affect the mechanical performance of nano-CPB and reduced the accelerating effect of nano-CaCO3 due to sulphate attacks.
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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 | 0.000 | 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".