Modeling or dynamic simulation: a tool for environmental management in mining?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it