Efficiency as a Road to Sustainability in Small Scale Mining
Bibliographic record
Abstract
The world is going through a new-millennium rush in precious metals, especially gold. The great increase in gold price in the last years, probably due to a shift towards safe investments in a period of crisis in the global economy, created a rapid increase in gold production. The faster response to this shift in production came from Artisanal (ASM) and Small-scale (SSM) mining units in remote locations of the world, and Brazil is one of the main countries that has ASM and SSM on its territory. The present paper draws some definitions of Small-Scale Mining and Artisanal Mining, based on its productivity and its actual social and environmental implications, and of their sustainability. The analysis of production data of Small Scale and Large Scale Mining on global scale and on Brazilian scale shows the high potential of SSM in dealing with lower mineral grades and market fluctuations, due to its high flexibility. A general growth of the role of SSM in precious metals production in the next decades is foreseen. An elaboration on world ASM data led to a clear correlation between efficiency in production and an index of human development; this result is shown and discussed. Based on the potential of SSM to attend to the mineral market needs, efficiency in productivity is finally proposed as the main path to turn an ASM unit into a sustainable and profitable Small-Scale industrial extractive unit.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".