{"id":"W4366286488","doi":"10.1186/s13326-023-00284-w","title":"Constructing a knowledge graph for open government data: the case of Nova Scotia disease datasets","year":2023,"lang":"en","type":"article","venue":"Journal of Biomedical Semantics","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cape Breton University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Linked data; RDF; Open government; Open data; SPARQL; Semantic Web; Data science; Graph; Vocabulary; Knowledge graph; Government (linguistics); Data cube; Information retrieval; Data mining; World Wide Web; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002193296,0.0001187298,0.0003291772,0.00009231103,0.0001150535,0.0001755469,0.003553869,0.00004926533,0.000007424436],"category_scores_gemma":[0.001098592,0.00007020697,0.00009645738,0.0006542475,0.0003473187,0.0004704763,0.002510428,0.0001637855,0.000006795538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003070623,"about_ca_system_score_gemma":0.0002785165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006255803,"about_ca_topic_score_gemma":0.0001855874,"domain_scores_codex":[0.9982899,0.00008738055,0.0006570211,0.0002300328,0.0004651968,0.0002704723],"domain_scores_gemma":[0.9972255,0.001037785,0.0005316769,0.0008659312,0.000117817,0.0002213527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002193355,0.00105977,0.005680812,0.0008340038,0.0007399422,0.01054459,0.001161488,0.00001435231,0.0004864492,0.05505637,0.5121414,0.4120615],"study_design_scores_gemma":[0.01544114,0.002887537,0.01432735,0.003430496,0.002115243,0.02732761,0.02283291,0.5127977,0.00367416,0.06153556,0.331635,0.001995354],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.15048,0.001977014,0.7874352,0.04682358,0.008221889,0.001281796,0.003459997,0.0000865785,0.000233947],"genre_scores_gemma":[0.9654205,0.0001378891,0.03379371,0.0002046881,0.0003423372,0.000001635126,0.00006637766,0.00001120795,0.00002163932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8149406,"threshold_uncertainty_score":0.6604033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08305450426609681,"score_gpt":0.3645655341344907,"score_spread":0.2815110298683938,"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."}}