Correlation between the Pore Structure and Water Retention of Cemented Paste Backfill Using Centrifugal and Nuclear Magnetic Resonance Methods
Bibliographic record
Abstract
This research combines a centrifugal test and nuclear magnetic resonance (NMR) technology to study the water retention capacity of the cemented paste backfill. Backfill samples with cement–tailings ratios of 1:4, 1:8, and 1:12, and solid concentrations of 71%, 74%, 77%, 80%, and 83% respectively, were prepared for the test. The relative centrifugal force ( RCF ) required for accurate testing and the T2 cutoff value that characterizes the water retention capacity were obtained through an NMR test on the backfill samples after centrifugation in saturated conditions. Based on the soil–water characteristic curve (SWCC), the NMR pore water characteristic distribution model was established, and the pore size distribution and effective water retention characteristics were analyzed. This study shows that when the rotating speed is between 1500 and 4000 rpm, the R C F of the backfill ranges from 125.8 to 894.4 g/min , and the T2 cutoff value will vary from 3 to 10 ms. With an increase in solid concentration of the backfill, both the RCF and T2 cutoff value decline. The Scanning Electron Microscope (SEM) analysis confirms that an increase in the solid concentration and cement–tailings ratio will lead to obvious bimodal characteristics of the pore size distribution curve of the backfill. In addition, the porosity will decrease, the critical pore value, which represents a value to distinguish pores with different movable fluid retention capabilities and characterizes the pore size classification, will become smaller, and the pore size distribution will become more diverse. These changes indicate that a high-concentration backfill can effectively reduce the flow of a fine-grained matrix with large pores.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".