Hydration Effects on Specific Gravity and Void Ratio of Cemented Paste Backfill
Bibliographic record
Abstract
Abstract This article presents the results of a laboratory study on the variation of specific gravity (Gs) and void ratio (e) in a cemented paste backfill (CPB) with different cement contents and curing times. CPBs consist of a mixture of mine tailings, portland cement, and mine process water. In this study, a helium stereopycnometer device was used to determine Gs for specimens having cement contents of 3.0, 5.3, 7.5, and 11.1 % by the weight of dry tailings. Curing times of 4 h, 1, 3, 7, 14, and 28 days were considered for testing purposes. In total, 240 helium stereopycnometer tests were conducted in this study, which shows that Gs decreases in the CPB specimens as specimens cure over time and the cement hydrates. Furthermore, 96 tests were conducted to determine e of the CPB specimens with different combinations of cement contents and curing times. The results of these tests indicate that similar to Gs, e decreases with an increase in curing time as the specimens hydrate. The reduction in Gs and e is attributed to the formation of hydration products, which have a lower density than the cement and fill the void space between the particles. The results show that the variation of Gs that is due to the hydration process should be considered in the calculation of e; otherwise, this physical property, especially for specimens with high cement contents, is overestimated.
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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 | 0.001 |
| 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".