Contribution au développement de nouveaux ciments économiques à empreinte carbone réduite destinés aux remblais miniers cimentés
Bibliographic record
Abstract
Mine backfilling, a common practice in underground mining, involves returning nearly half of the mine tailings (generated by the ore processing) in the form of cemented paste backfill (CPB) to fill underground voids. This practice offers multiple economic, safety, and environmental benefits. However, the use of CPB is heavily dependent on the market and the availability of binders, which are either pure Portland cement (PC) or PC mixed with supplementary cementitious materials (fly ash, blast furnace slag, etc.). For instance, PC, which is highly polluting to produce, and slag have become more expensive than ever. Additionally, transporting the binder to mines, especially those far from urban areas, adds extra costs and increases their carbon footprint.This thesis project aims to develop alternative binders that are more economical and environmentally friendly, focusing on the use of locally available materials for their production to reduce the costs and carbon footprint of CPB. Four alternatives are being explored, including alkali-activated binders, the valorization of steelmaking ladle furnace slags and flue gas desulfurization dusts, as well as the use of clays from the Témiscabitibi region as sources for limestone-calcined clay cements (LC3).The project results show very promising prospects both in research and industrial application, without requiring extensive additional research. The development of cementitious formulations within the framework of this thesis was based on simulated backfill (using fine sand) and an average curing time (up to 28 days). Therefore, for all developed formulations, real backfill applications (using mine tailings) and testing over different time frames (beyond 28 days) are necessary.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".