Consistency and Rheological Properties of Cemented Paste Backfills Prepared with Tailings with Varying Free Muscovite Content
Bibliographic record
Abstract
The presence of free muscovite in tailings can negatively affect the mechanical strength and rheological properties of cemented paste backfill, as has been observed for several cementitious materials. The aim of this study is to evaluate the influence of free muscovite content in tailings on the consistency and rheology of cemented paste backfill. For this purpose, cemented paste backfill mixtures were prepared from two different tailings. The mixtures were prepared at solids contents between 70% and 74% and with the addition of 5% GU (general use Portland cement)/slag binder. In addition, the influence of muscovite was studied by varying the muscovite content of the tailings from about 14% to 25%. Abrams cone slump tests and rheological analyses were carried out for each recipe. The results show a decrease in slump height and an increase in yield stress, Herschel–Bulkley flow index, and infinite shear rate Cross viscosity with increasing muscovite content for a given solids content. Therefore, water should be added to maintain the required flowability of cemented paste backfill, which increases the water/binder ratio and may affect the mechanical strength. A method is presented for determining the amount of binder to be incorporated to maintain the water/binder ratio of the original cemented paste backfill recipe.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".