{"id":"W2785659847","doi":"10.2139/ssrn.3157479","title":"How Much Does Anticipation Matter? Evidence from Anticipated Regulation and Land Prices","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Anticipation (artificial intelligence); Economics; Monetary economics; Financial economics; Econometrics; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002294675,0.0001537728,0.0003757345,0.0002920303,0.0003331254,0.001903215,0.00056021,0.001460285,0.008673939],"category_scores_gemma":[0.02078414,0.0002731599,0.0003389043,0.0005498881,0.001357078,0.001579553,0.0006776853,0.001302211,0.0008089912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003539631,"about_ca_system_score_gemma":0.0003361954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003882363,"about_ca_topic_score_gemma":0.004448656,"domain_scores_codex":[0.9992557,0.0003079274,0.00004960165,0.0001419298,0.0001299851,0.0001149308],"domain_scores_gemma":[0.9461853,0.03548584,0.01363186,0.001814302,0.001575991,0.001306843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00418117,0.0009574526,0.9425501,0.0001944632,0.0004716121,0.0004872126,0.001109154,0.002163627,0.001634656,0.01476602,0.004227605,0.02725702],"study_design_scores_gemma":[0.0001549493,0.0004233519,0.9774572,0.00004070996,0.0002569084,0.00009677715,0.001296941,0.001945881,0.0007621235,0.01183495,0.005695973,0.00003434627],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981574,0.001116047,0.0007100177,0.004180773,0.00006874797,0.000008539857,0.0003384459,0.000009411446,0.01199403],"genre_scores_gemma":[0.9985488,0.000275584,0.00005261394,0.0002454061,0.0000560033,0.000002387072,0.0001616001,0.000004060926,0.000653472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008673939,"threshold_uncertainty_score":0.02901721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02322241492533084,"score_gpt":0.227680032381884,"score_spread":0.2044576174565532,"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."}}