{
  "author": "Giovana Mancilla Pivato; Gustavo Venâncio da Silva; Beatriz Granetti Peres; Stelio Pacca Loureiro Luna; Monique Danielle Pairis-Garcia; Pedro Henrique Esteves Trindade",
  "citations": [
    {
      "raw_text": "Scientific Reports | (2025) 15:7161 | https://doi.org/10.1038/s41598-025-91551-6",
      "title": "Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique",
      "authors": "Giovana Mancilla Pivato; Gustavo Venâncio da Silva; Beatriz Granetti Peres; Stelio Pacca Loureiro Luna; Monique Danielle Pairis-Garcia; Pedro Henrique Esteves Trindade",
      "publication_year": 2025,
      "doi": "10.1038/s41598-025-91551-6",
      "url": "https://doi.org/10.1038/s41598-025-91551-6",
      "confidence": 0.98
    }
  ],
  "confidence": 0.95,
  "doc_type": "Research Article",
  "id": 36,
  "language": "en",
  "publication_date": "2025",
  "source_type": "file",
  "source_uri": "file:///home/pi/welfare_docs/Animals Assured/Research/Pig Castrate/s41598-025-91551-6.pdf",
  "summary": "This study proposes a 'Short UPAPS' for assessing acute pain in surgically castrated pigs, using a random forest algorithm to identify the five most important pain-altered behaviours. This short version maintains predictive accuracy comparable to the original UPAPS.",
  "title": "Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique"
}