An Investigation into the Potential Use of Calcium Carbonate Nanoparticles in Mine Backfill
Bibliographic record
Abstract
This study is part of large and ongoing investigation into the potential use of calcium carbonate nanoparticles (NCaCO3) to improve the mechanical and physical properties of mine backfill. The investigation was conducted in two phases. In phase I, gold tailings were used to prepare backfill sample using Portland cement (PC) as the binding agent under various NCaCO3 and superplasticizer (SP) dosages. In phase II, fine silica sand was used to minimize the reactive nature of tailings, which can adversely affect uniaxial compressive strength (UCS). Samples in this phase were prepared under various NCaCO3 dosages using slag:PC weight ratios of 80:20 as the binding agent. Select samples from phase II were subjected to Mercury Intrusion Porosimetry (MIP) to compare microstructural properties before and after addition of NCaCO3. Phase I results showed that the UCS of samples containing NCaCO3 was low, even when SP was used to improve particle dispersion. Phase II samples exhibited up to 9.5% higher UCS after 28 days of curing at a NCaCO3 dosage of 6% by weight of binder. MIP results showed that samples containing NCaCO3 were less porous than the corresponding controlled silica sand backfill samples, which in turn influenced the UCS results.
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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".