Analysis of trajectories and developmental prospects of research on carlin-type gold deposits on the basis of big data community detection algorithms
Notice bibliographique
Résumé
• Pioneering Methodology: Applies graph-based community detection (CiteSpace) to map the 56-year knowledge trajectory and developmental prospects of Carlin-type gold deposit research, overcoming limitations of traditional reviews. • Theoretical Advancement: Proposes the universally applicable “multi-source fluids – tectonic activation – nano-scale occurrence” system and the spiral cognitive model (phenomenon → mechanism → system → prediction), extending relevance beyond Carlin-type deposits. • Actionable Pathways: Provides technology-driven solutions (multiscale characterization, AI-enhanced prospecting, eco-extraction) and global collaboration frameworks to address challenges in reserve expansion, sustainable extraction, and theory refinement. The rise of big data analytics and knowledge graph technology has introduced a new paradigm for research on mineral deposits. This study employs CiteSpace, a graph-based community detection tool, to analyze the Web of Science Core Collection literature (1969–2025) on Carlin-type gold deposits, with the aim of identifying global research trajectories, collaboration networks, key themes, frontiers, and future directions. The evolution of research encompasses five distinct phases: the Foundational Period (1969–1990), Domain-expanding Period (1991–2000), Refinement Period (2001–2010), Integration Period (2011–2020), and the ongoing Transformative Leap Period (2021–2025). Geographically, studies have expanded from their origin in Nevada, U.S., to a global scale. Methodologically, advancements have progressed from macro-geological mapping to atomic-scale characterization, accompanied by a theoretical shift towards an integrated “multi-source fluids – tectonic activation – nano-scale occurrence” system. This progression follows a spiral cognitive model: phenomenon description → mechanism analysis → system modeling → predictive application. The international collaboration network has evolved into a “dual-core leadership with multi-tier synergy” framework, where core nations (China and the U.S.) drive cutting-edge theoretical exploration by leveraging their giant ore clusters, while secondary nodes (e.g., Canada, Iran, Australia) enhance research scope and depth through critical regional analogues and cross-deposit-type expertise. Emerging participants (e.g., Malaysia) inject new dynamism and alternative genetic perspectives. Research leadership has transitioned from early dominance by U.S. institutions (e.g., USGS) to prominence of Chinese entities (e.g., Chinese Academy of Sciences, China University of Geosciences) post-2010. Core research themes include: (1) ore formation-regional tectonic coupling, (2) ore-forming fluid dynamics, (3) microscopic gold occurrence and mineralization processes, (4) resource utilization challenges, and (5) integration of multi-technique methodologies and intelligent exploration. Current research frontiers focus on: metallogenic chronology and geodynamic settings, multi-source fluid evolution and tectonic-lithologic coupling, invisible gold occurrence mechanisms, exploration technology innovation and deep targeting, and integrated studies across diverse deposit types. Future priorities center on two pillars: (1) Technological innovation: integrating techniques such as APT, NanoSIMS, and in situ isotopic methods for “atom-mineral-deposit-region” multiscale modeling; applying machine learning to overcome deep-prediction bottlenecks for intelligent “geology-geochemistry-geophysics-remote sensing” prospecting; and developing eco-leaching/microbial oxidation processes for the efficient extraction of gold and associated critical elements (As, Sb, Hg, Fe, S) from refractory ores. (2) International collaboration: establishing unified deposit testing standards; creating a global data-sharing platform; and deepening strategic partnerships through core-core, core-secondary, and core-emerging nation collaborations. These coordinated advancements are poised to drive breakthroughs in reserve expansion, extraction efficiency, sustainable resource development, and the refinement of metallogenic theory
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».