Study of the effects of arsenic trioxide roaster waste dusts on the mechanical behaviour of cemented paste backfills
Bibliographic record
Abstract
More than 237,000 metric tonnes of arsenic trioxide roaster waste dusts have been stored underground in the abandoned Giant Mine (Yellowknife, NWT). This waste arsenic trioxide material is approximately 60% arsenic, which is hazardous to both people and the environment. Long-term management of this waste is complex due to its large quantity, physical characteristics, and current storage conditions. Currently, the frozen block method was selected for the stabilisation of the arsenic. However, because of climate change and decline in the permafrost, as well as the current toxic form of dusts, there are some critical concerns about the long-term performance of this technology. Therefore, more permanent arsenic stabilisation techniques must be considered. Among these techniques, cemented paste backfill (CPB) technology as a high-density slurry mixture of binding materials, dewatered tailings, roaster waste dusts, and mixing water, can be considered as a potential arsenic trioxide stabilisation method. In this research, the effects of the addition of the arsenic trioxide dusts (10% wt.) within CPB on the mechanical strength of the cured pastes were evaluated. The CPB samples were prepared using general use (GU) cement and a mixture of GU cement and lime kiln dust (LKD) as binding agents based on the mix designs proposed by the response surface methodology. The solid content, binder type and dosage, and curing time were selected as the variables and the unconfined compressive strength of the samples was chosen as the response of the modelling. The results of the experiments and analyses revealed that the incorporation of arsenic trioxide dusts within the CPB results in the reduction in the strength of the pastes. However, increasing binder dosage, as well as solid content, could compensate for this adverse effect. Moreover, the CPB prepared using GU cement showed higher strength than the ones incorporating GU cement/LKD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".