ASSESSMENT OF THE IMPACTS OF NEW MINING TECHNOLOGIES: RECOMMENDATIONS ON THE WAY FORWARD
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
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.
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 | 0.000 |
| 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 it