Green mining techniques to curb environmental problems - A review
Bibliographic record
Abstract
Abstract Many methods are used to extract the ores causing huge threat to environment. Mining practices lead to un sustainability and the problems created by it were not yet controlled. So adoption of Green mining technologies helps to attain sustainable development and control the problems to maximum extent. The main objective of green mining is to start mining process and end it to ensure that adopting green mining practices lead to sustainability. Green mining also reduces greenhouse gases prone to effect conserve minerals, using energy more efficiently etc. According to Mission 2016 plan many green mining techniques were adopted and increased focus on research and became more popular so every industry focusing on environmental friendly technologies. Some of the major mining nations like Canada, Australia, South Africa etc adopted sustainable development viewed on not only environment but also other dimensions like local stake holder engagement, socio economic development in mining project areas and transparency in communication with stake holders. Sustainable strategies of mining includes measurement, monitoring mainly to improve the performance of environment and ensure that the mining operations are perfect or not. This paper reviews impacts of mining in various countries and Green mining solutions adopted over the world. It also discussed about Green supply chain management and the barriers of it and given the suggestions to control these barriers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".