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Pairs of scientific disciplines whose data live on the sphere — for example cosmology / astrophysics, climate, Earth observation, marine biodiversity — with shared HEALPix substrate but discipline-specific background spectra and feature-location distributions.
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For two scientific discipline regimes that share the HEALPix substrate but differ in their background statistics and feature-location distributions, does a sphere-aware machine-learning approach trained on one discipline classify the other discipline without retraining at higher accuracy than an equivalent lat-lon-flat machine-learning approach trained identically, measured by cross-domain transfer accuracy?
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Does sphere-aware ML on HEALPix transfer cleanly across discipline pairs without retraining?
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