{"id":"W4404414979","doi":"10.70088/8f92z263","title":"An Empirical Study on the Determinants of Housing Prices in Beijing and Model Optimization","year":2024,"lang":"en","type":"article","venue":"Science, technology and social development proceedings series.","topic":"Korean Urban and Social Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Beijing; Econometrics; Computer science; Business; Economics; Geography; China; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000643318,0.00009695745,0.0001312941,0.0002400341,0.0009367734,0.00008584619,0.0001859738,0.00007705266,0.00000390964],"category_scores_gemma":[0.00004091778,0.00007004167,0.0000070147,0.001439488,0.001681784,0.0004705983,0.0002435319,0.0001269461,7.473074e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009427537,"about_ca_system_score_gemma":0.00004848414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003950142,"about_ca_topic_score_gemma":0.00004097551,"domain_scores_codex":[0.9990689,0.000003951026,0.0001691712,0.000318258,0.0002309182,0.0002087839],"domain_scores_gemma":[0.9998747,0.00001114207,0.00004475053,0.00003075118,0.00001695047,0.00002170429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009467111,0.00007108261,0.9358386,0.00001334757,0.000004584189,0.000001893933,0.05085614,0.00004656596,0.0005129127,0.005255047,0.0000141447,0.007376245],"study_design_scores_gemma":[0.0003424074,0.0004486946,0.8047363,0.0001014012,0.00002183921,0.000006024812,0.1477625,0.03357476,0.003068769,0.009050863,0.0003720623,0.0005143353],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997727,0.00002855915,0.00005215999,0.0005558896,0.00002775345,0.000206259,3.649689e-7,0.00005845313,0.001343512],"genre_scores_gemma":[0.9981133,0.00002251956,0.001755182,0.00004327107,0.000005847899,0.00002240033,9.806558e-8,0.000005131464,0.00003228452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1311022,"threshold_uncertainty_score":0.7204998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02085861611085667,"score_gpt":0.2819385987532715,"score_spread":0.2610799826424148,"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."}}