Predicting Dinosaur Coloration from Fossilized Melanosome Data Using Classical Machine Learning

This study establishes a reproducible classical machine-learning baseline for dinosaur coloration reconstruction from fossilized melanosomes, while systematically evaluating cross-clade generalization. By comparing QDA, SVM, and Random Forest on theropod and non-theropod taxa, the study demonstrates that high-confidence predictions for phylogenetically distant specimens reflect distribution shift and morphological overlap with the avian training data rather than reliable biological coloration. These findings highlight the limitations of transferring bird-trained models across dinosaur clades and motivate more taxonomically broad and taphonomically informed approaches to paleocolor reconstruction