Modeling or dynamic simulation: a tool for environmental management in mining?
Bibliographic record
Abstract
The buoyancy of the minerals market, due to price and demand continually rising, maintains an increased interest for investors in mining. However, it is a sector particularly facing many negative environmental impacts, technical and environmental conditions to which are added the meeting of financial and production goals. Nevertheless in lockstep together, risk management of these extractive activities on environment – in this age where the society’s level of awareness in ecological balance has evolved – continues to fuel discussions and interventions. Therefore, it becomes unavoidable to manage more effectively the environmental factors around mines. This study aims to propose the integration of environmental management (EM) tools based on dynamic simulation (DS) for mining. This research is structured in four main topics: (1) the dynamics of open-pits system, (2) the management of their environmental effects, (3) the EM tools at the disposal of managers and (4) the proposed EM by DS. The results show that the challenges are numerous and the volume of DS approaches in mining is constantly growing, even if only few are directed towards EM. Some approaches of DS in a few open-pits with the proven effectiveness, show a new opportunity to investigate.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".