Geomechanical aspects of stabilizing arsenic trioxide roaster waste in cemented paste backfill at the Giant Mine, Canada
Bibliographic record
Abstract
Giant Mine, an abandoned gold mine near Yellowknife in the Northwest Territories of Canada, generated significant arsenic trioxide roaster waste (ATRW) during its operations, posing a substantial environmental hazard. This study explored the feasibility of stabilizing ATRW by incorporating it into cemented paste backfill (CPB). Using response surface methodology (RSM), CPB samples with varying mixing ratios were analyzed to identify key parameters influencing strength. Unconfined compressive strength (UCS) tests assessed physical stability, while saturated hydraulic conductivity and computed tomography (CT) analyses examined the microstructure of the CPB. The results revealed that CPB samples prepared with general use (GU) cement exhibited significantly higher strength than those with a GU and lime kiln dust (LKD) mixture. Binder and solid contents were identified as the most critical factors influencing UCS, with binder content having a more pronounced influence. Curing time was found to be non-significant. Higher binder and solid contents correlated with higher UCS values in the CPB samples. The addition of 10% wt. ATRW reduced the UCS by over 30%, particularly in samples with lower binder and solid contents. Although microstructure differences were not evident in saturated hydraulic conductivity tests, CT scans showed increased formation of high-density arsenic-containing materials in samples with the highest UCS, especially those using GU binder. These findings suggest that optimizing binder and solid contents is crucial for enhancing CPB strength and effectively stabilizing ATRW.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".