Tools to improve mine closure: 10 years of research in integration of environment in the mine life cycle
Bibliographic record
Abstract
Abstract Mine closure can be approached by several points of view, from the technical, engineering, ecological, up to social and governance aspects. The definition of a good mine closure should cover most, if not all, of these aspects. This article provides a review of technical and engineering-oriented research work as a partial answer to the question ‘what is good mine closure’. The article presents a ten-year research program realized in the framework of a Canada Research Chair in integration of environment in the mine life cycle. Research projects aimed at better planning mine closure and mine site reclamation from the early stages of a mining project life cycle are exposed as possible steps to strive for optimal mine waste management. At the exploration stage, geo-environmental characterization and modeling are proposed as tools to improve mine waste management planification. During mine operations, environmental desulfurization is suggested as a method to reduce environmental risks associated with sulfidic tailings and waste rock. Indeed, research has shown that acid mine drainage and metal leaching can be significantly limited via desulfurization. At the closure stage, desulfurized tailings can be used to replace at least part of natural materials used for reclamation cover systems. Research work done on other types of mine waste, such as waste rock and water treatment sludge, also show good potential for their reuse as closure material. All these tools can be integrated into the mine life cycle to better plan for closure, which ultimately will make mine closure more sustainable.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".