{"id":"W2073724362","doi":"10.1177/0141076815578597","title":"The impact of E.F. Lindquist's text on cluster randomisation","year":2015,"lang":"en","type":"article","venue":"Journal of the Royal Society of Medicine","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Cluster (spacecraft); Computer science; Text mining; World Wide Web; Information retrieval; Data science; Natural language processing; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004944474,0.000096107,0.000334107,0.00001969946,0.00007451365,0.00001232488,0.0008205696,0.00006416618,0.000005358875],"category_scores_gemma":[0.0004770076,0.00003567092,0.0004887855,0.0001476832,0.0002246225,0.00006700652,0.00008536883,0.0002830166,4.939569e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007628265,"about_ca_system_score_gemma":0.0001783756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004635024,"about_ca_topic_score_gemma":5.981285e-7,"domain_scores_codex":[0.9983639,0.000298921,0.0004983582,0.00007263803,0.0006404862,0.0001257269],"domain_scores_gemma":[0.9978452,0.0005136518,0.0008435557,0.0003301884,0.0003637228,0.0001037328],"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":[0.001557264,0.0002511165,0.0006515862,0.0000532558,0.0006871753,0.000002256193,0.02123693,0.01092473,0.001512159,0.004990152,0.724454,0.2336794],"study_design_scores_gemma":[0.06421549,0.00998698,0.02151688,0.002120162,0.0004503357,0.0002076869,0.001386026,0.6813683,0.007289637,0.1970955,0.01384367,0.0005193824],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01999937,0.001088018,0.95083,0.02577356,0.0007877965,0.0001330061,5.714507e-7,0.000003670981,0.001384029],"genre_scores_gemma":[0.9544917,0.0001088579,0.04304471,0.0008174503,0.0008057693,7.093469e-7,1.057468e-7,0.000007665933,0.0007230596],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9344923,"threshold_uncertainty_score":0.1713666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02657730332539639,"score_gpt":0.3148389513184571,"score_spread":0.2882616479930608,"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."}}