{"id":"W3188726618","doi":"","title":"주택유형별 매매가격 변동의 상호관계에 관한 실증연구 - 서울지역을 중심으로 -","year":2019,"lang":"ko","type":"article","venue":"부동산경영","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Apartment; Quarter (Canadian coin); Shock (circulatory); Economics; Econometrics; House price; Financial economics; Geography; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000333112,0.0001124013,0.0001812492,0.0008210388,0.0002370519,0.000833569,0.0002089169,0.0001467123,0.003266075],"category_scores_gemma":[0.001245589,0.000134424,0.0003260327,0.001339516,0.0001978474,0.0009864945,0.0003023591,0.0002941422,0.0006372659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000407776,"about_ca_system_score_gemma":0.0003044083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003867723,"about_ca_topic_score_gemma":0.00940407,"domain_scores_codex":[0.9996605,0.00004544927,0.00003972773,0.00009146126,0.0001079136,0.00005498528],"domain_scores_gemma":[0.9989754,0.0002145972,0.0004333841,0.00005739893,0.0002421439,0.00007716707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008156095,0.00007640359,0.9768033,0.00005052667,0.00005393092,0.0002450176,0.0008245574,0.0006057322,0.002555961,0.0009687843,0.0004071179,0.01732709],"study_design_scores_gemma":[0.000001541236,0.0000575041,0.9955511,0.000003275281,0.00001052636,0.00009860753,0.001106322,0.001639502,0.000518351,0.0001909435,0.0008166207,0.000005764959],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976282,0.00003506649,0.0005688188,0.0000316968,0.000003966601,0.00001235857,0.0002718053,0.000004532126,0.001443729],"genre_scores_gemma":[0.9984194,0.00002482059,0.0003918657,0.00001061956,0.000003385151,0.000007786541,0.0003816376,0.000002414091,0.0007581598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003867723,"threshold_uncertainty_score":0.01092607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817865960268062,"score_gpt":0.1908390120765975,"score_spread":0.1726603524739169,"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."}}