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Sphere matched filter holds 1.000 accuracy at every test latitude band; flat collapses to 0.500 at 70–80
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The sphere-harmonic band-pass matched filter on the HEALPix-NESTED substrate maintains 1.000 detection accuracy and 1.000 F1 across all four test latitude bands while the equivalent lat-lon flat matched filter trained on the same data collapses progressively as the test latitude moves poleward, dropping to 0.500 accuracy and 0.000 F1 (chance, all-negative classification) at 70-80°. 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.
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Numerical results from notebook 04 — flat lat-lon matched filter at test bands {0–20°: accuracy 1.000, F1 1.000; 30–40°: 1.000, 1.000; 50–60°: 0.915, 0.907; 70–80°: 0.500, 0.000} versus sphere-harmonic band-pass matched filter at the same bands {0–20°: 1.000, 1.000; 30–40°: 1.000, 1.000; 50–60°: 1.000, 1.000; 70–80°: 1.000, 1.000}. Training accuracy 1.000 for both pipelines on the in-distribution low-latitude set.
200 test samples per band (100 positive + 100 negative).
Github repository: https://github.com/annefou/spherical-ml-biodiversity
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(i) Synthetic SST with controlled feature physics — this isolates the substrate effect from the model class but does not characterise real-data noise patterns.
(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.
(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. (iv) Four test latitude bands sample the failure mode at four points; intermediate latitudes were not separately tested.
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Anne Fouilloux
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