Bibliographic record
Abstract
This book is aimed to help us look into the future of mining by defining ultimate operational conditions that will be present in a typical mining operation regardless of how far in the future. It introduces an innovation strategy designed to identify current and future technologies to achieve specific ultimate operational conditions that will be present in ‘the mine of the future’. The mining innovation strategy presented here is the result of several innovation projects where the author had the opportunity to assist and had successfully implemented it at several mining companies and mining research institutions around the world, including Australia, South Africa, the United States, Canada, Peru, and Mexico. This innovation strategy is designed to be consistent with any type of mining method as well as any commodity being mined, such as metal or nonmetal mining, soft-rock or hard-rock mining, underground or surface mining. The five ultimate mining operational conditions or drivers discussed in this book were carefully defined considering current operational and technology trends, which will keep any mining company competitive during the following decades. The mining innovation strategy thus considers five ultimate operational conditions or drivers (1) Achieving maximum safety, (2) simplifying systems, (3) using smart-intelligent systems, (4) designing stealth operations, and (5) sustainable use of environmental and human resources within the operation. These five innovation drivers are common denominators to any mining method, regardless of their operational nature or commodity being mined either today or in the future. It is thus envisaged that the mining innovation model introduced in this book can serve as an initial guideline for the mining industry to better identify current and future technologies that need to be addressed today.
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.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
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".