Deep Learning Framework Classifies and Counts Mineral Grains Without Segmentation Labels
A new deep learning framework segments, classifies and counts microscopic mineral grains with 97.57% accuracy without requiring annotated segmentation masks.
A new deep learning framework segments, classifies and counts microscopic mineral grains with 97.57% accuracy without requiring annotated segmentation masks.
Sulfur trapped in zircon-hosted apatite inclusions shows that oxidative weathering and recycling of surface materials began raising the oxygen fugacity ...
Nanoscale examination of saprolite shows that even crystalline zircon begins to dissolve and reprecipitate as uranium- and thorium-bearing amorphous zircon ...
Scientists at the University of Colorado Boulder have built and calibrated a custom low-volume helium analysis line, the Jimbochron, that ...
A new analysis shows that uncertainties in helium diffusion parameters in zircon can shift predicted thermochronologic ages by hundreds of ...
Researchers at the University of Calgary have developed a new modeling strategy that uses syn-depositional zircon grains and combined U/Pb ...
A new study dissects the physical origins of uranium-lead measurement bias in laser ablation zircon dating and shows that directly ...
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© 2025 Scienmag - Science Magazine