The new mineral exploration strategies of selected major mineral-rich countries
Bibliographic record
Abstract
This paper first describes basic information on 13 mineral resource strategy reports issued by the world’s major mineral resource exploration countries and regions, including Australia, Canada, Europe, the U.S., Russia, and India. Through these strategic reports, we identified the problems facing current mineral exploration and development, such as mining issues, increased on land access and permitting, disincentives to obtain precompetitive geoscience information, and the urgent need to improve exploration technology to adapt to new demands. Then, by studying the visions and aims of the new mineral resource strategies, this paper found that the strategic goals have something in common: to display a new image of mining development. The new image of mining development is an image of advanced mining through green development, ecological protection, technology intensity, sustainability, and social acceptance, consolidating the primary position and foundational role of mineral resources and mining development in economic and social development. The new image creates a favorable development environment for the rational use and adequate protection of mineral resources. After that, a summary of the measures taken to achieve these objectives, which include strengthening domestic mineral exploration, increasing coordination between mineral exploration and ecological environmental protection, strengthening the life cycle management of the industrial chain, playing a significant role in scientific and technological innovation, and paying close attention to significant shifts in the focus on critical minerals, is provided.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".