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.
A new review in Ionics summarizes how defect engineering, through surface delithiation and bulk vacancy modulation, can improve the air ...
Laser-based microanalysis of biotite in the contact aureole of Labrador's Makhavinekh Lake Pluton shows that rubidium–strontium ages depend on grain-scale ...
Researchers have developed a laser ablation mass spectrometry method that yields reliable uranium-lead ages from garnet containing less than one ...
Geologists have documented only the tenth known well-preserved lawsonite eclogite on Earth, in Türkiye's Central Pontides, revealing peak subduction conditions ...
A 4.4-million-year-old coesite eclogite from Papua New Guinea reveals that young subduction zones begin with warm geothermal gradients of about ...
A new review details how engineering active ceramic fillers such as garnet, NASICON, perovskite, and sulfide materials into solid polymer ...
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© 2026 Scienmag - Science Magazine