Experimental and Computational Assessment of Tailings Binder Matrices for Construction Purposes in Cold Regions
Bibliographic record
Abstract
Mine tailings are the waste materials of the mining industry. They are typically disposed of in tailings ponds surrounded by tailings dams. This traditional method of disposal has caused severe environmental damage throughout the years. In this study a new approach of sustainable development of tailings is attempted. The study consisted of two phases – experimental and computational. In Phase 1, six different types of tailings are gathered from mines in Eastern Canada and subjected to a series of laboratory tests. Tailings were stabilized using different compositions of binder materials: Portland cement, slag, fly ash along with a new type of binder called Calsifrit. These experiments aimed at verifying the suitability of tailing-binder matrices as road construction material. Furthermore, weathering tests assessed feasibility of using the matrices in cold regions. \n \nIn Phase 2 a computational program was developed using the Discrete Element Method to support the engineer’s decision with regards to the application of the binder tailing materials in construction. \n \nExperimental results show that these tailings binder matrices passed the freezing/thawing durability and TCLP tests. In addition, these matrices sustained high compression loads. Using these results, a statistical equation is developed to predict the unconfined compressive strength of the tailings binder matrices. Simulations show that the computer program developed was able to model successfully the unconfined compressive strength and freezing/thawing durability characteristics of the tailings binder matrices.
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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 | 0.001 | 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) | 0.000 | 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".