Global Mining at the Edge of Transformation
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
Early this year, mining giant Rio Tinto opened a new data center in Pune, India, to analyze massive volumes of information gathered from sensors attached to equipment operating at its mines around the world. Rio Tinto's Analytics Excellence Centre was established to predict and prevent downtime and improve safety and productivity. It's an industry first--mining has been pretty much last in adopting such business practices--but it could be the beginning of a sorely needed transformation. The center is unique in applying sophisticated data management technology, but also because it manages multiple, remote operations from a central location--mining has been, traditionally, a local art. If it is successful, the center and other recent innovations like it could help alter the fortunes of an industry in crisis. is a volatile business at any time, subject to boom-or-bust cycles and the vagaries of nature, local politics, and commodity markets. But 2014 was a particularly bad year in a string of them, and 2015 looks bleak as well. Gold prices are at a four-year low. Iron ore plummeted to double-digit territory in May 2015 and is forecast to hit an all-time low of $70 a ton in 2017. The world's biggest steel consumer, China, is pulling back on orders as its economy slows. Many mining companies have canceled projects or folded altogether. In this context, the world's largest mining companies are fighting to survive and stay competitive. Some companies have invested in critical new automation and drilling technology that promises to improve production and reduce costs, but a growing chorus of industry leaders argues that more fundamental change is needed--companies must change from within. Business models and processes must be streamlined to eliminate waste, smooth out the peaks and valleys of production, reduce accidents, protect the environment, and provide flexibility to respond to the unforeseen. The industry's future, these leaders say, depends not so much on where the next big ore deposit is discovered, but on how well mining companies coordinate dynamic information across complex operations. Emilie Ditton, a mining industry consultant and head of Asia Pacific Energy Insights at International Data Corporation (IDC), spends much of her time identifying ways to get operational silos inside mining companies to talk with each other. Mining companies are very sophisticated within their operational silos, she said. They do everything from optimizing truck performance to minimizing fuel costs and conveyor belt material handling efficiency. But if a production operation meets its goals and delivers product to a processing operation that can't handle all of the material coming in, the ultimate production outcomes are not improved. companies have assets, technology, and access to data, Ditton said. What they require is an enterprise-level data Far more than new technology, she said, mines need organizational investment in data interpretation as a business strategy. But, she admits, it takes strong leadership to take a risk on a new idea. We don't yet have a convincing story to tell on why we would take that risk, she said. That might be changing, however. We're in the early stages of a paradigm shift in mining, said Mike MacFarlane, a Canadian engineer and industry consultant, and retired executive vice president of AngloGold Ashanti, one of the world's largest mining companies. There are lots of clues around. Start with technology and innovation. In the United States, the auto industry in the 1940s and '50s had no peers; in the '60s and '70s, no peers. In the '80s, little Japanese cars changed everything. GM went to Japan and copied Toyota's lean manufacturing process. I would say the mining industry, in the little Japanese car analogy, is in the mid-80s. Indeed, the metaphor holds in the particulars, too, as mining companies are beginning to look at lean manufacturing for ways to streamline their own operations. …
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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 | 0.001 | 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