Mitigation of Environmental Hazards and Greenhouse Gas Emissions Through Use of Post-Consumer Glass as a Cementing Agent in Mine Backfill
Bibliographic record
Abstract
This paper examines aspects of mine backfilling operations that may be implemented to assist in reducing solid waste production, lowering energy costs, and restricting Greenhouse Gas Emissions. Many underground mines use cemented backfill to provide ground support and to mitigate environmental effects associated with tailings disposal. In Ontario, mining operations contribute approximately 700,000 - 840,000 tonnes/year of CO2and Greenhouse Gas emissions to satisfy backfill cement consumption needs alone. Potential reductions of cement in backfill would significantly reduce emission of these gases and lower their adverse environmental impacts. This paper introduces post-consumer glass as an alternate, equally effective and lower cost binder agent strategy for backfill that may be implemented to assist in reducing backfill energy costs; and ultimately reducing Greenhouse Gas emissions levels generated by the mining industry. Extensive engineering testing, consisting of pipe flow loop tests and strength tests, has been implemented in order to demonstrate the technical feasibility of process integration within industry. An economic analysis has demonstrated that glass is competitive in cost relative to cement and a socioeconomic study has further indicated that the utilization of glass in mine backfill would create a new market for waste glass that requires less processing and reduces costs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".