{"id":"W3027132030","doi":"10.1007/978-3-030-62466-8_28","title":"The OpenCitations Data Model","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Università di Bologna; Université de Montréal; Universiteit Leiden; Wellcome Trust","keywords":"Computer science; Ontology; Reuse; Variety (cybernetics); Reusability; Context (archaeology); Semantic Web; Information retrieval; Data model (GIS); Data science; World Wide Web; Data mining; Engineering; Artificial intelligence; Software; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.007069092,0.00113314,0.001363257,0.01656298,0.002906875,0.0137947,0.005646911,0.003861032,0.0388994],"category_scores_gemma":[0.03674273,0.0009538112,0.001892837,0.03156418,0.001757583,0.01435858,0.006901034,0.003767912,0.02692553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006758606,"about_ca_system_score_gemma":0.01190492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04774516,"about_ca_topic_score_gemma":0.03512357,"domain_scores_codex":[0.9886838,0.001950271,0.00277481,0.00162143,0.004368292,0.0006015077],"domain_scores_gemma":[0.9702711,0.007094627,0.002349391,0.009462941,0.009507528,0.001314401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002355643,0.0001166635,0.004028329,0.001195005,0.0001080797,0.0005206969,0.0008883002,0.008484332,0.0009086903,0.5028611,0.4161389,0.0645142],"study_design_scores_gemma":[0.0000270071,0.000009921058,0.0004836739,0.000151603,0.00002781809,0.0001493843,0.0001355304,0.002896853,0.0006005848,0.04308251,0.9523923,0.00004278761],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.005487769,0.001424608,0.2394551,0.007822203,0.001160051,0.001549554,0.5517104,0.0275159,0.1638745],"genre_scores_gemma":[0.03917726,0.00226679,0.1446924,0.002629658,0.0004944436,0.003351022,0.7625347,0.004974861,0.03987898],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9862053,"threshold_uncertainty_score":0.1301314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1031986247926079,"score_gpt":0.3319555588165707,"score_spread":0.2287569340239629,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}