Copernicus potential for sustainable development to improve governance and management of mineral resources
Bibliographic record
Abstract
[ENG] This study thoroughly examines the potential of the Copernicus program to transform the governance and management of mineral resources towards sustainable development. Divided into four sections, it advances the fusion of cutting-edge Earth observation techniques with environmentally friendly methods of managing mineral resources. A comprehensive overview of the study's significance, scope, motivation, goals, and objectives, laying the groundwork for the subsequent investigations is provided. Contextual foundations of the Copernicus program and its applicability to mineral resource management were analyzed. Detailed discussions cover Copernicus technology, remote sensing technologies (SAR and multispectral), the Sentinel missions 1, 2 and 5P, the significance of mineral resources for sustainable development, complemented by the United Nations protocols UNC & UNRMS, the INSPIRE Directive and the current status of Earth observation projects within the European Union. Two systematic literature reviews were conducted as well. The first explores the integration of remote sensing technologies with Sustainable Development Goals (SDGs), aiming to identify potential applications in improving mineral resource governance and monitoring the impact of mining activities over time. The second investigates the interrelationships between Copernicus and Key Enabling Technologies (KETs), offering insights into technological advancements relevant to mineral resource management. Two case studies were conducted in Abanilla-Murcia, Spain, and the oil sands mining assets of Suncor Energy in Alberta Province in Canada, validating theoretical insights from the literature review with empirical data, providing a nuanced analysis of Copernicus technology's applicability in real-world scenarios for monitoring environmental impact of mining operations, vegetation health and air quality around the selected mining areas. Findings were synthetized from the literature review and the two case studies, discussing implications for advancing Copernicus technology in sustainable development and mineral resource management. Business opportunities identified, contributions to academic and practical understanding, and future research directions are also outlined. Fresh perspectives and useful applications to the field were provided, contributing to the broader goal of sustainable development and effective resource management. Suggesting new lines for future research related o the integration of additional satellite technologies and data sources to further enhance monitoring accuracy and governance capabilities on mineral resources from Earth and celestial bodies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".