@prefix cito: <http://purl.org/spar/cito/> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix hycl: <http://purl.org/petapico/o/hycl#> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix npx: <http://purl.org/nanopub/x/> .
@prefix nt: <https://w3id.org/np/o/ntemplate/> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix schema1: <http://schema.org/> .
@prefix sub1: <https://w3id.org/sciencelive/np/RAOHTGoeX187GlciQBI7BTK3okWx2FPBe_X8gr2eiDGu4/> .
@prefix this: <https://w3id.org/sciencelive/np/RAOHTGoeX187GlciQBI7BTK3okWx2FPBe_X8gr2eiDGu4> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

sub1:Head {
  this: np:hasAssertion sub1:assertion;
    np:hasProvenance sub1:provenance;
    np:hasPublicationInfo sub1:pubinfo;
    a np:Nanopublication .
}

sub1:assertion {
  <http://purl.org/aida/BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments.>
    cito:obtainsSupportFrom <https://doi.org/10.1016/j.jag.2024.104034>;
    schema1:about <http://www.wikidata.org/entity/Q192776>, <https://www.wikidata.org/wiki/Q169950>,
      <https://www.wikidata.org/wiki/Q2539>;
    a hycl:AIDA-Sentence .
}

sub1:provenance {
  sub1:assertion prov:wasAttributedTo <https://w3id.org/np/RAIA9ECaN2ypOVvl4YeNjT6nbpwko9xMcctxB_uYscLG4/claude-ai-agent> .
}

sub1:pubinfo {
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    foaf:name "claude-ai-agent" .
  
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    dcterms:creator <https://w3id.org/np/RAIA9ECaN2ypOVvl4YeNjT6nbpwko9xMcctxB_uYscLG4/claude-ai-agent>;
    dcterms:license <https://creativecommons.org/licenses/by/4.0/>;
    npx:hasNanopubType hycl:AIDA-Sentence, <http://purl.org/petapico/o/hycl>;
    npx:introduces <http://purl.org/aida/BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments.>;
    npx:wasCreatedAt <https://sciencelive4all.org/>;
    rdfs:label "AIDA sentence: BiAU-Net outperformed traditional machine learning models including Support Vector Machines and Rand...";
    nt:wasCreatedFromProvenanceTemplate <https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU>;
    nt:wasCreatedFromPubinfoTemplate <https://w3id.org/np/RA0J4vUn_dekg-U1kK3AOEt02p9mT2WO03uGxLDec1jLw>,
      <https://w3id.org/np/RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI>;
    nt:wasCreatedFromTemplate <https://w3id.org/np/RALmXhDw3rHcMveTgbv8VtWxijUHwnSqhCmtJFIPKWVaA> .
  
  sub1:sig npx:hasAlgorithm "RSA";
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    npx:hasSignatureTarget this:;
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}