• Deterministic culture: single models become entrenched as institutional truth over time. Uncertainty is culturally equated with incompetence rather than scientific honesty. • Software ecosystem: current commercial software reinforces deterministic workflows. Highperformance computing (needed for Monte Carlo sampling) is expensive and inaccessible to small companies. • Junior mining model: most exploration is funded on a short-term, one-borehole-at-a-time basis. This structurally discourages the multi-hypothesis, long-horizon approach the paper advocates. A portfolio-based investment vehicles can serve as a structural fix—analogous to diversified equity portfolios—where risk and return are traded off across a set of prospects rather than evaluated one at a time. Extending the JORC reporting standard to require uncertainty quantification, modeled on oil & gas practice, is much needed. 5. Sustainability and Education 5.1 Sustainable Exploration Environmental and social sustainability should be integrated into exploration decision-making from the outset, not appended after resource delineation. The key observation is that sustainability impact is inversely proportional to deposit grade: a high-grade underground operation has a far smaller footprint than a low-grade open pit. This means that targeting high-grade deposits is simultaneously the most economical and most environmentally responsible strategy. Sustainability metrics—social license, jurisdictional regulations, environmental impact—should be quantified alongside economic metrics in portfolio-level analysis. 5.2 Knowledge and Education The arrival of large language models has accelerated a long-overdue transformation in how domain knowledge is created, accessed, and taught. Of concern is the declining enrollment in economic geology and mining engineering in Western countries, the risk of losing century-old knowledge stored in aging reports, and the absence of integrated ‘Mineral Exploration’ curricula that combine geoscience with decision science and data science. Priority recommendations include digitizing and open-sourcing national geological databases, creating web-accessible platforms for historical exploration data, and redesigning curricula around problem-solving rather than discipline-only training. Building a shared language between geoscientists and data scientists is identified as essential. 6. Summary The exploration industry’s declining discovery rate is primarily a methodological problem, not a resource scarcity problem. The proposed fix—Popper-Bayes exploration enabled by AI—replaces single-model determinism with multi-hypothesis uncertainty quantification, and replaces ad hoc drilling with sequentially optimal data acquisition. Several components of this framework already exist in research-grade implementations. The bottleneck is integration: assembling them into a usable platform, combined with cultural change in the industry, new financial instruments, and updated regulatory standards. This change will not happen overnight, and that both the industrial and academic communities must move together for it to take hold.
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