@prefix this: <https://w3id.org/np/RAn4ChpN8nXUYA_z1gDSMA_pN95nm5CmmKksIpOLM3L74> .
@prefix sub: <https://w3id.org/np/RAn4ChpN8nXUYA_z1gDSMA_pN95nm5CmmKksIpOLM3L74/> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix nt: <https://w3id.org/np/o/ntemplate/> .
@prefix npx: <http://purl.org/nanopub/x/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix orcid: <https://orcid.org/> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .

sub:Head {
  this: a np:Nanopublication;
    np:hasAssertion sub:assertion;
    np:hasProvenance sub:provenance;
    np:hasPublicationInfo sub:pubinfo .
}

sub:assertion {
  sub:comparatorGroup dct:description "Lat-lon-flat machine learning trained identically on the first discipline's data and applied to the second discipline's data via the equirectangular projection, exploiting only translation equivariance in pixel space." .
  
  sub:interventionGroup dct:description "Sphere-aware machine learning trained on one discipline's data and applied to another discipline's data on the shared HEALPix substrate without retraining, exploiting rotation equivariance on the sphere." .
  
  sub:outcomeGroup dct:description "Detection accuracy on the second discipline's test set without retraining (cross-domain transfer accuracy), with in-domain accuracy on each discipline as sanity check and upper bound." .
  
  sub:population dct:description "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." .
  
  sub:spherical-ml-cross-discipline-transfer-2026 a <http://data.cochrane.org/ontologies/pico/PICO>,
      <https://w3id.org/sciencelive/o/terms/EffectivenessResearchQuestions>;
    <http://data.cochrane.org/ontologies/pico/comparatorGroup> sub:comparatorGroup;
    <http://data.cochrane.org/ontologies/pico/interventionGroup> sub:interventionGroup;
    <http://data.cochrane.org/ontologies/pico/outcomeGroup> sub:outcomeGroup;
    <http://data.cochrane.org/ontologies/pico/population> sub:population;
    dct:description "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?";
    rdfs:label "Does sphere-aware ML on HEALPix transfer cleanly across discipline pairs without retraining?" .
}

sub:provenance {
  sub:assertion prov:wasAttributedTo orcid:0000-0002-1784-2920 .
}

sub:pubinfo {
  orcid:0000-0002-1784-2920 foaf:name "Anne Fouilloux" .
  
  this: dct:created "2026-05-07T21:09:34.069Z"^^xsd:dateTime;
    dct:creator orcid:0000-0002-1784-2920;
    dct:license <https://creativecommons.org/licenses/by/4.0/>;
    npx:introduces sub:spherical-ml-cross-discipline-transfer-2026;
    npx:wasCreatedAt <https://nanodash.knowledgepixels.com/>;
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