{"id":"W2782809622","doi":"10.3968/10065","title":"The Relationship Between the Basic Medical Insurance and the Commercial Medical Insurance in the Old Age Security in China","year":2017,"lang":"en","type":"article","venue":"Canadian social science","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medical insurance; Actuarial science; Reimbursement; Point (geometry); Insurance policy; Medical underwriting; Key person insurance; China; Business; Casualty insurance; Group insurance; General insurance; Security system; Economics; Income protection insurance; Health care; Computer science; Computer security; Law; Economic growth; Political science; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008198614,0.0001691972,0.0003724216,0.001383675,0.0009437369,0.001256899,0.0005709752,0.0004747059,0.002793143],"category_scores_gemma":[0.001965262,0.0002650303,0.0005982926,0.0013488,0.000726447,0.0008715225,0.001197698,0.0008027718,0.0001010012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002460651,"about_ca_system_score_gemma":0.001767924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07370181,"about_ca_topic_score_gemma":0.09185958,"domain_scores_codex":[0.9993784,0.0001039413,0.00004818825,0.0001006708,0.0001176488,0.0002511095],"domain_scores_gemma":[0.9982527,0.000396788,0.0005509324,0.00005953259,0.000209518,0.0005303932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000485686,0.00008515133,0.9811184,0.00003195941,0.000130994,0.0006841012,0.0009267562,0.001508255,0.0004300607,0.009319623,0.0004720046,0.005243996],"study_design_scores_gemma":[0.000009031245,0.00004378244,0.9830574,0.00002304582,0.00009431662,0.0001920631,0.001301022,0.01067664,0.0001071979,0.003359339,0.001114917,0.00002118505],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978459,0.0002704721,0.0002241586,0.0006271789,0.000005420381,0.000004989347,0.00006667535,0.000002612826,0.0009525515],"genre_scores_gemma":[0.9994718,0.00008743716,0.00003307249,0.00001974817,0.00000640532,0.000001435476,0.00003183464,6.157155e-7,0.000347589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07370181,"threshold_uncertainty_score":0.1465456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06717452497429713,"score_gpt":0.3139993633831052,"score_spread":0.2468248384088081,"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."}}