Innovation in mining: what are the challenges and opportunities along the value chain for Latin American suppliers?
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".