ASSESSMENT OF THE IMPACTS OF NEW MINING TECHNOLOGIES: RECOMMENDATIONS ON THE WAY FORWARD
Bibliographic record
Abstract
Rapid growth in technological innovation in the mining sector is having a fundamental impact on the mining landscape. Innovation fuelled by automation, digitization, and electrification have led to the introduction of autonomous vehicles, automated drilling and tunnel boring systems, drones, and smart sensors. While these new technologies could contribute to improved profit margins, reduced greenhouse gas emissions, and improved worker health and safety, they could also have significant impacts on local employment levels, skills creation, and local content in mining projects. Emerging technologies may also give rise to new types of environmental and occupational health problems, due to for example, the emissions of nanomaterials. Hence, new technologies may warrant a reassessment of project impact assessment categories, as some categories that may be relevant for assessing new technologies may not exist yet, whereas some that do exist may not be relevant. Hence, organisations conducting project assessments should prepare and respond to these technological shifts in the mining sector. This paper highlights some technological innovations and their potential socio-economic and environmental impacts on communities. It also assesses the impact of innovation on the environmental assessment and regulatory processes for mines. Recommendations on ways of assessing the biophysical, environmental and socio-economic impacts of new technologies are outlined.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.017 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".