This Perspective argues that as artificial intelligence is increasingly built into Earth system models, reproducibility needs to be rethought to match how these hybrid models actually work. The authors highlight risks such as numerical instability, opaque procedures and unequal access to computing power, which can undermine trust and traceability in climate research. They propose a … Read more…
Researchers compared fission track counts made by a trained AI system (HALtracks 2D) with those of an expert human analyst using identical microscope images of apatite and mica samples. The AI matched human counts closely on clean, simple grains and performed comparably to the average expert even on more complex samples, though both struggled with … Read more…
Abstract. The global transition to clean energy is sharply increasing demand for rare earth elements (REEs), yet discovery rates are declining, especially in areas concealed by younger cover. Deep learning (DL) offers new opportunities for mineral prospectivity mapping (MPM), but its application is challenged by sparse labelled mineral occurrences, strong class imbalance, and limited model … Read more…
Rare earth elements (REEs), a significant subset of critical minerals, play an indispensable role in modern society and are regarded as “industrial vitamins,” making them crucial for global sustainability. Geochemical survey data proves highly effective in delineating metallic mineral prospects. Separating geochemical anomalies associated with specific types of mineralization from the background reflecting geological processes … Read more…