{"id":"W3125377897","doi":"10.2139/ssrn.1026270","title":"Canadian City Housing Prices and Urban Market Segmentation","year":2007,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bank of Canada; Queen's University; Government of Canada","funders":"","keywords":"Market segmentation; Segmentation; Business; Economics; Economic geography; Microeconomics; Artificial intelligence; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006771068,0.000306717,0.0004296549,0.002339955,0.002923907,0.003180893,0.001059522,0.0008889515,0.02100207],"category_scores_gemma":[0.005049926,0.0003234794,0.0006357133,0.006693396,0.0009118119,0.001144159,0.0008749557,0.0008199434,0.0008295343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04587665,"about_ca_system_score_gemma":0.02526881,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9959351,"about_ca_topic_score_gemma":0.99858,"domain_scores_codex":[0.9994079,0.0000557314,0.00001825083,0.0000788068,0.0001688613,0.0002704984],"domain_scores_gemma":[0.9961644,0.0005127097,0.0006538536,0.0001438983,0.001546523,0.0009786633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005676849,0.0001419181,0.8923102,0.00009273089,0.0002130354,0.0002576386,0.003146385,0.003818669,0.0002463921,0.0250037,0.0487202,0.02548143],"study_design_scores_gemma":[0.00002236278,0.00001372479,0.9836633,0.00003479424,0.00004021433,0.000028462,0.002736466,0.002727507,0.00008812665,0.001343018,0.009270995,0.00003117624],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9284122,0.002734484,0.0004078131,0.005504619,0.00007246149,0.00004839744,0.01534123,0.00005030811,0.04742844],"genre_scores_gemma":[0.985788,0.0005656729,0.0001538086,0.0001517248,0.00002410328,0.000009283341,0.00292527,0.00001988408,0.01036214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04587665,"threshold_uncertainty_score":0.33286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01088774545058925,"score_gpt":0.2015229803182048,"score_spread":0.1906352348676156,"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."}}