Innovation in mining: what are the challenges and opportunities along the value chain for Latin American suppliers?
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
Abstract The mining industry, considered a traditional and conservative industry with respect to innovation, finds itself at a turning point due to the increasingly complex challenges, such as declining ore grades. These challenges have created an imperative to innovate. Parallel to the above, several digital innovations are being implemented in many mining operations across the globe. Not only do these provide solutions to the existing problems but also radically transform mining processes, increasing efficiency, profitability, and the ability to comply with stricter regulations. The incorporation of mature and incipient technologies into the mining industry has opened up many opportunities for long-established firms as well as knowledge-based start-ups. This includes potential suppliers in countries where mining accounts for a significant share of the GDP but the development of productive linkages remains suboptimal, as in Latin American countries. While in recent years, some suppliers in Latin America have made important contributions to increasing innovation in the mining industry, most suppliers in the region have not been able to do so. This paper provides an overview of the innovation paradigm of the mining sector from a global perspective, i.e., how innovation processes take place in countries with a long-established technological leadership in the mining sector, such as Australia and Canada. Given the importance of suppliers in this process, a special attention is paid to innovation in various stages of the supply chain. This is in order to provide a departure point for identifying windows of opportunity for equipment and service suppliers in Latin America.
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