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Both pipelines use identical feature structure and identical logistic-regression heads; the only thing that differs is the substrate. The sphere-harmonic operation is exactly rotation-equivariant on the sphere by construction, so the response peak for the same physical spherical cap is the same value regardless of where on the sphere it sits; the lat-lon flat operation is only translation-equivariant in pixel space, so the same physical cap renders to a longitudinally-stretched shape at high latitudes and the matched-filter response degrades." } } } rows { name { value: "hasConfidenceLevel" } } rows { name { value: "HighConfidence" } } rows { quad { p_iri { } o_iri { } } } rows { name { value: "hasEvidenceDescription" } } rows { quad { p_iri { } o_literal { lex: "Numerical results from notebook 04 \342\200\224 flat lat-lon matched filter at test bands {0\342\200\22320\302\260: accuracy 1.000, F1 1.000; 30\342\200\22340\302\260: 1.000, 1.000; 50\342\200\22360\302\260: 0.915, 0.907; 70\342\200\22380\302\260: 0.500, 0.000} versus sphere-harmonic band-pass matched filter at the same bands {0\342\200\22320\302\260: 1.000, 1.000; 30\342\200\22340\302\260: 1.000, 1.000; 50\342\200\22360\302\260: 1.000, 1.000; 70\342\200\22380\302\260: 1.000, 1.000}. Training accuracy 1.000 for both pipelines on the in-distribution low-latitude set.\n200 test samples per band (100 positive + 100 negative). \n\nGithub repository: https://github.com/annefou/spherical-ml-biodiversity" } } } rows { name { value: "hasLimitationsDescription" } } rows { quad { p_iri { } o_literal { lex: "(i) Synthetic SST with controlled feature physics \342\200\224 this isolates the substrate effect from the model class but does not characterise real-data noise patterns. \n(ii) The cosine-of-latitude SST baseline is an idealised tropical-to-polar gradient; real SST climatologies have basin-scale and seasonal structure the high-pass filter would also remove if extended to lower multipoles. \n(iii) The minimal (max, mean, std) feature triple is the simplest possible matched-filter readout; richer learned representations from DeepSphere graph convolutional networks or foscat scattering networks would deliver substantially higher feature dimensionality on real data. 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