Slag-based stabilization/solidification of hazardous arsenic-bearing tailings as cemented paste backfill: Strength and arsenic immobilization assessment
Bibliographic record
Abstract
The widespread occurrence of toxic arsenic in sulfidic and non-ferrous waste tailings hinders its disposal as cement paste backfill (CPB). Alkali activated slag (AAS) has recently begun to be practiced as an alternative to normal Portland cement (OPC). Nevertheless, technical information on arsenic immobilization and mechanical characteristics of arsenic-rich AAS-CPB is rather few. The impacts of activator nature, cure temperature and arsenic content on strength and arsenic immobilization of AAS-CPB explored. Despite AAS-CPB having greater strength, OPC-CPB consistently has a stronger (1.7–21.1% higher) ability to immobilize arsenic. The optimum silica modulus for maximal strength and arsenic immobilization capability depends on curing time. Strength at3 days is enhanced by higher activator doses, whereas strength at later ages (≥ 28 days) is decreased. At all curing ages, the lowest arsenic immobilization capacity is produced by medium activator concentration (0.35). Irrespective of cement type, strength increases as curing temperature rose, however OPC-CPB's strength is more responsive to temperature changes than AAS-CPB's. At room temperature (20°C), OPC-CPB has a higher (6.0–21.1% greater) arsenic immobilization efficiency (AIE) than AAS-CPB, but the opposite is true at lower (5°C) and higher (35°C) temperatures (i.e., 5.4–12.0% and 4.4–12.0% lower at 5 and 35°C respectively). Early on, the influence of arsenic content on strength is not immediately apparent, but it tends to become more obvious with longer curing times. As a role of cement type and elapsed time, high arsenic contents cause a rise or a decrease in AIE. Notably, there is no apparent connection between UCS and AIE. Electrical conductivity and moisture content can be steadily employed to portray the hydration progression of both arsenic-free and arsenic-containing CPB.
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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".