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https://w3id.org/sciencelive/np/RA7g02DAdAV4zUy1_u7_lD7GnTDPkbnpyUIsfGDmv-yyE

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Content

@prefix this: <https://w3id.org/sciencelive/np/RA7g02DAdAV4zUy1_u7_lD7GnTDPkbnpyUIsfGDmv-yyE> .
@prefix sub: <https://w3id.org/sciencelive/np/RA7g02DAdAV4zUy1_u7_lD7GnTDPkbnpyUIsfGDmv-yyE/> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix npx: <http://purl.org/nanopub/x/> .
@prefix dc: <http://purl.org/dc/terms/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

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

sub:assertion {
  <https://doi.org/10.48550/arXiv.1703.05175> <http://purl.org/spar/cito/hasQuotedText>
      "Prototypical networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. ";
    <http://www.w3.org/2000/01/rdf-schema#comment> "This foundational few-shot learning method was tested on mini-ImageNet (natural photos). We test whether the learned metric space transfers to Sentinel-2 satellite imagery for land cover classification — a fundamentally different visual domain where rare habitat types have very few labeled examples. " .
  
  <https://w3id.org/np/o/ntemplate/CREATOR> <http://purl.org/spar/cito/quotes> <https://doi.org/10.48550/arXiv.1703.05175> .
}

sub:provenance {
  sub:assertion prov:wasAttributedTo <https://orcid.org/0000-0002-1784-2920> .
}

sub:pubinfo {
  <https://orcid.org/0000-0002-1784-2920> <http://xmlns.com/foaf/0.1/name> "Anne Fouilloux" .
  
  this: dc:created "2026-04-17T11:22:05.965Z"^^xsd:dateTime;
    dc:creator <https://orcid.org/0000-0002-1784-2920>;
    dc:license <https://creativecommons.org/licenses/by/4.0/>;
    npx:hasNanopubType <http://purl.org/spar/cito/cites>;
    npx:wasCreatedAt <https://platform.sciencelive4all.org>;
    <http://www.w3.org/2000/01/rdf-schema#label> "Paper annotation: Prototypical networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. ";
    <https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate> <https://w3id.org/np/RA24onqmqTMsraJ7ypYFOuckmNWpo4Zv5gsLqhXt7xYPU> .
  
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    npx:hasSignatureTarget this:;
    npx:signedBy <https://orcid.org/0000-0002-1784-2920> .
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