{"id":"W2118947320","doi":"","title":"광역시 주택가격 변화의 특징과 요인 분석","year":2008,"lang":"ko","type":"article","venue":"국토연구","topic":"Korean Urban and Social Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; House price; Falling (accident); Economics; Demographic economics; Price index; Panel data; Population; Quarter (Canadian coin); Geography; Econometrics; Demography","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":[0.0001365207,0.0001131465,0.0001011054,0.0008283112,0.0002072468,0.0004722938,0.0001783412,0.0001147155,0.002015362],"category_scores_gemma":[0.0004459282,0.00008677261,0.0001517764,0.001711341,0.0001711009,0.0004416487,0.0002109862,0.0001541054,0.000358086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005328298,"about_ca_system_score_gemma":0.0002324808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02699563,"about_ca_topic_score_gemma":0.06738207,"domain_scores_codex":[0.9999045,0.000007031553,0.00001094417,0.0000304549,0.00001879184,0.00002816736],"domain_scores_gemma":[0.9995584,0.00002316655,0.0002518368,0.00001587672,0.0001128106,0.00003788862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006249399,0.00002234678,0.9790825,0.00006569312,0.00005736548,0.0003122474,0.000967064,0.0004558932,0.0009490238,0.0008552609,0.00174027,0.01542993],"study_design_scores_gemma":[7.511398e-7,0.00001625867,0.9960986,0.000004497762,0.00001208043,0.00006649744,0.0008850835,0.000285788,0.0002028004,0.00005146105,0.002372021,0.000004031391],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961849,0.0002814522,0.0001294727,0.0001270349,0.000006790238,0.000004100335,0.000928672,0.000004918234,0.002332737],"genre_scores_gemma":[0.9972377,0.000261165,0.0001066029,0.00002393986,0.000008460018,0.000002963776,0.00105921,0.000002065331,0.00129782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02699563,"threshold_uncertainty_score":0.05367702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02288993940954977,"score_gpt":0.209572893143734,"score_spread":0.1866829537341842,"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."}}