{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001095492,0.0005525961,0.001217822,0.0004471784,0.0002338669,0.0004955016,0.0007758588,0.0005741632,0.0136764],"category_scores_gemma":[0.0001259957,0.0007446675,0.0004811755,0.0003817774,0.0001324693,0.0006353803,0.0003003002,0.000583657,0.0639588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004190685,"about_ca_system_score_gemma":0.0001186021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004691412,"about_ca_topic_score_gemma":0.00009641618,"domain_scores_codex":[0.9961254,0.00004159271,0.001387673,0.00121077,0.000071006,0.001163539],"domain_scores_gemma":[0.9973754,0.0001682501,0.0007560683,0.001323049,0.00004514268,0.0003320625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002010347,0.0005298336,0.8516046,0.0004876137,0.0004916991,0.00003808416,0.001642466,0.0006835414,0.00006737901,0.08527353,0.03464599,0.0243342],"study_design_scores_gemma":[0.002293043,0.0002706239,0.07560969,0.0001133797,0.00005204286,0.00001914096,0.0002676967,0.01699644,0.00009763346,0.01264763,0.8899347,0.00169794],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6294258,0.001485386,0.0001451763,0.001357409,0.006433563,0.000444542,0.0002522292,0.00009475369,0.3603611],"genre_scores_gemma":[0.95788,0.003181077,0.0004432848,0.001129473,0.001034713,0.00001734122,0.00007547736,0.0001588939,0.03607978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8552887,"threshold_uncertainty_score":0.9995005,"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."}}