. . . . . . "Cross-discipline transfer of sphere-harmonic vs lat-lon-flat matched filters between cosmology-like and climate-like synthetic domains on HEALPix-NESTED " . . . . . . . "First in-repository demonstration of cross-discipline transfer with this matched-filter pair; no prior single-paper precedent for this specific experiment. The two synthetic regimes are constructed to share feature physics across different background spectra so the substrate effect is isolated from the model class." . . "Two synthetic discipline regimes are generated as Gaussian random fields directly on HEALPix-NESTED, distinguished by their angular power spectra and an optional latitudinal baseline. The first regime represents a cosmology-like domain (steeper power spectrum, no latitudinal baseline, features placed at uniformly-random sphere locations); the second represents a climate-like domain (shallower power spectrum, cosine-of-latitude baseline approximating an SST equator-pole gradient, features confined to high latitudes). The same physical feature physics — same angular radius and same amplitude — is used in both. The sphere-aware pipeline applies a sphere-harmonic band-pass matched filter (high-pass to suppress the latitudinal baseline plus a Gaussian beam matched to the feature scale) and reads out the maximum, mean, and standard deviation of the response field. The lat-lon-flat baseline cross-correlates the equirectangular projection of the same data with a feature-shape template at the equator and reads out the same three statistics. Both feed identical logistic-regression heads. Each pipeline is trained on the first regime only and evaluated three ways: in-domain on the first regime (sanity), cross-domain on the second regime without retraining (the headline test), and in-domain on the second regime when trained directly on it (upper bound). The full pipeline is reproducible via the repository's environment.yml and Snakefile." . "We test the cross-discipline transfer aspect of the substrate-dependence claim. Scope: two synthetic discipline regimes that share the same physical feature physics (same compact-feature size and amplitude) but differ in their stochastic background and in the latitudinal distribution of where features appear. The classifier is trained on the first discipline only and applied to the second discipline without retraining; the comparator is a lat-lon-flat baseline trained identically on the same first-discipline data. The within-discipline regime (same feature distribution at training and test) is out of scope here." . . . "Anne Fouilloux" . "2026-05-07T21:54:59.055Z"^^ . . . . . "NP created using Declaring a replication study design according to FORRT" . . "RSA" . "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" . "dqPqy9PUj8ADhsRj6QCUVN0RssY9eABMqsJZ0YO+vhaE925N+Du8Sp9XZLlXRH75iol9XpRAo3x39ZuYvFgFwHK51D259sHxg+k0rCuDFSyf83j+QcGsfESnJZC3ZyGxDjS8Ow0/W+rpzfdUcZkskND2CADYeKdCJlh93QQWfQXnpTqE9+mHozIKa6HjXRWefxirTFE5zkNz5I8tWiiJEbMdSGcoUE30kulYZwMUUrNYIUHJPL3a3UvzDIlzMyEwQAnE6romFIUgIhpxbB4Um1geZq6sT1NC6NOjiWWJ6Qq7ty3dWRIy5idszBi40DQc5XVVlGeFjwHEB5yQoyYesjuJJDMkoK+i9X/n3KUT5bJH3FkXsciVoO5XPGSosnBoVjkP3yM3qecuJ4XogqAtPY6S5JmkPLBCYBB91QVY9wLk3vOS881GfaGRlosm2kWY3PA3aa4vUeQ5DdYqW34Liy8qncyBFSyPkpUNvqi/ZGmTtSf9SqPA8KQQkz+xBeX9w/FA4xNX6i1pukPHWsLCjZNGLJzs1UAFerNQgHeIsR/FUDjyK/NPbjl14uaEAOK9iNADJ4JDs7RqZd6eWYXDtRGmNZdIidz4W4PZJgGGeyzNoUj+NCOdDehXj07s7eGShK3YfCHOl/hmWpIVjlmVmKyx9DwtWMdkOciJhsvCoy8=" . . .