{"id":"W2978303577","doi":"","title":"Factors affecting housing prices: a case study in China","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Distributed lag; Quarter (Canadian coin); Inflation (cosmology); China; Economics; Autoregressive model; Lag; Price index; Econometrics; House price; Macroeconomics; Geography","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":[0.0006716858,0.000372458,0.0003210546,0.001335676,0.00109594,0.0007396576,0.0006139931,0.0006478108,0.001024179],"category_scores_gemma":[0.0009736792,0.0002145536,0.000552217,0.002594078,0.0005344924,0.0005839348,0.0007142343,0.000403571,0.00009945784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002252081,"about_ca_system_score_gemma":0.00196664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1389759,"about_ca_topic_score_gemma":0.1485284,"domain_scores_codex":[0.9995489,0.0001042805,0.00003135785,0.0000565197,0.0001261283,0.000132765],"domain_scores_gemma":[0.9994397,0.000170468,0.0001346556,0.00003914858,0.0001209241,0.00009512348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001229668,0.0005472946,0.9347973,0.0001403002,0.0000968602,0.02567202,0.006367711,0.005852006,0.001445711,0.002238582,0.00133011,0.02138924],"study_design_scores_gemma":[0.00003176507,0.0003645645,0.9476647,0.00003780933,0.00008848685,0.002814273,0.01723606,0.02676525,0.0009854278,0.000729639,0.003234727,0.00004742064],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987131,0.00009202067,0.0001903996,0.0001122407,0.000002529725,0.00002295848,0.00008204924,0.000003055169,0.0007817054],"genre_scores_gemma":[0.9981449,0.0003150526,0.000389524,0.00002784566,0.00001019701,0.0000149471,0.000129026,0.000001900566,0.0009666064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1389759,"threshold_uncertainty_score":0.2763339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04429321379394962,"score_gpt":0.2605384064196527,"score_spread":0.2162451926257031,"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."}}