{"id":"W2543755080","doi":"10.1177/0193945916673815","title":"Explorative Analyses of Nursing Research Data","year":2016,"lang":"en","type":"article","venue":"Western Journal of Nursing Research","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cancer Care Ontario","funders":"National Institutes of Health; National Institute for Health and Care Research; Patient-Centered Outcomes Research Institute","keywords":"Metadata; Standardization; Scope (computer science); Computer science; Metadata repository; Meta Data Services; Big data; Data element; Nursing; Data science; World Wide Web; Medicine; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04858485,0.0008646008,0.000893899,0.02614629,0.002111721,0.005033856,0.001426256,0.0007761757,0.00210047],"category_scores_gemma":[0.1409651,0.0004283399,0.00159556,0.02316608,0.00226101,0.003578692,0.005882413,0.001069848,0.0003647105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002514165,"about_ca_system_score_gemma":0.005292445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003821048,"about_ca_topic_score_gemma":0.00593074,"domain_scores_codex":[0.9496486,0.03237599,0.006109505,0.002680168,0.007577564,0.001608098],"domain_scores_gemma":[0.7458587,0.2045595,0.01673965,0.01812928,0.01367437,0.001038549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009231843,0.0006001222,0.4029517,0.00719766,0.001583068,0.006618744,0.2560493,0.003398903,0.01995096,0.06971034,0.01094164,0.2200743],"study_design_scores_gemma":[0.0001088888,0.0006239515,0.3914868,0.003950749,0.00104148,0.003996606,0.345433,0.01809233,0.01911421,0.08238176,0.1333975,0.0003726933],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8405756,0.002581828,0.1117711,0.004689809,0.0002437771,0.0037266,0.02219324,0.000549548,0.01366847],"genre_scores_gemma":[0.8423733,0.0007705603,0.1398681,0.0006954159,0.0001152338,0.004892466,0.009547741,0.0001537374,0.001583494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04858485,"threshold_uncertainty_score":0.2569443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6548863625811201,"score_gpt":0.6113940098487308,"score_spread":0.04349235273238927,"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."}}