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Record W2201025148

Global Mining at the Edge of Transformation

2015· article· en· W2201025148 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueResearch-Technology Management · 2015
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)BoomBustAnalyticsBusinessProductivityInformation technologyCommodityEngineeringIndustrial organizationEconomyEconomicsComputer scienceFinanceEconomic growthData science
DOInot available

Abstract

fetched live from OpenAlex

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. …

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.074
GPT teacher head0.324
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it