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The sphere-harmonic band-pass matched filter trained on a cosmology-like domain (uniformly-random feature locations, steep background spectrum) classifies a climate-like domain (high-latitude features, smoother background plus cosine-of-latitude baseline) at 1.000 accuracy without retraining. The equivalent lat-lon flat matched filter trained identically drops from 1.000 in-domain on the cosmology-like domain to 0.845 on the cross-domain transfer to the climate-like domain because the equator-shape template under-responds to polar-stretched features in lat-lon space. Both pipelines reach 1.000 / 0.995 in-domain on the climate-like domain when trained directly on it, so the test is fair — the asymmetry shows up only in the cross-discipline transfer column. This is the operational claim that investments in sphere-aware models from one discipline (astrophysics / cosmology, where the HEALPix ML stack is most mature — DeepSphere, foscat, healpy) carry over to other disciplines (climate, biodiversity, Earth observation) on the shared HEALPix substrate without retraining." } ], "https://w3id.org/sciencelive/o/terms/hasConfidenceLevel": [ { "@id": "https://w3id.org/sciencelive/o/terms/HighConfidence" } ], "https://w3id.org/sciencelive/o/terms/hasEvidenceDescription": [ { "@value": "Numerical results from notebook 06 — sphere-aware {A→A in-domain 0.990; A→B transfer 1.000; B→B upper-bound 1.000} versus lat-lon-flat {A→A in-domain 1.000; A→B transfer 0.845; B→B upper-bound 0.995}. 200 training samples + 100 test samples per class per domain. Identical (max, mean, std) features and identical logistic-regression classifier heads on both pipelines. Reproducible end-to-end via the repository's environment.yml + Snakefile. \n\nGithub repository: https://github.com/annefou/spherical-ml-biodiversity" } ], "https://w3id.org/sciencelive/o/terms/hasLimitationsDescription": [ { "@value": "(i) Synthetic domain regimes constructed to share feature physics across different background spectra; true cross-discipline transfer from real cosmology data (e.g. Planck CMB on HEALPix) to real climate data (e.g. DLWP-HEALPix forecasts on HEALPix) would require integrating with foscat scattering networks or a DeepSphere graph convolutional network as future work. \n(ii) The substrate effect is isolated from the model class via the minimal (max, mean, std) feature triple; richer learned representations would deliver substantially different absolute accuracy numbers but the substrate-dependence is the geometric mechanism the experiment captures. \n(iii) The latitude restriction in the climate-like domain is the regime where lat-lon projection distortion bites hardest; the cross-discipline transfer test would yield different numbers for differently-distributed feature regimes. \n(iv) Two domains tested; the transfer-between-pairs claim generalises naturally to N-way transfer but was not separately tested with three or more domains. 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