{"id":"W3149623574","doi":"","title":"How Much Does Anticipation Matter? Evidence from Anticipated Regulation and Land Prices","year":2018,"lang":"en","type":"article","venue":"Duo Research Archive (University of Oslo)","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Anticipation (artificial intelligence); Economics; Identification (biology); Empirical evidence; Boundary (topology); Quarter (Canadian coin); Public economics; Econometrics; Macroeconomics; Geography","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":[0.004088701,0.0001296657,0.000378258,0.0003557473,0.0002724031,0.00160444,0.0004219387,0.0008456752,0.00506293],"category_scores_gemma":[0.0313962,0.0002309828,0.0004998508,0.0005523625,0.001393609,0.001995436,0.0007447958,0.001000271,0.0003216576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005392383,"about_ca_system_score_gemma":0.0004341504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004565828,"about_ca_topic_score_gemma":0.004775759,"domain_scores_codex":[0.9985206,0.0006755989,0.00009154344,0.000293804,0.000227393,0.0001911819],"domain_scores_gemma":[0.922542,0.05328762,0.0195399,0.002035427,0.001283112,0.001312002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007229035,0.0002692833,0.9656841,0.0001046225,0.0003518586,0.0001348106,0.0006498805,0.00332144,0.0006118684,0.0115473,0.001098086,0.0155039],"study_design_scores_gemma":[0.00003037098,0.0001678228,0.9833762,0.00003522228,0.0001265599,0.00003675701,0.0006551865,0.004349915,0.0003703723,0.009036031,0.001797117,0.00001845962],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849157,0.0006747656,0.002169102,0.002718582,0.00003265243,0.00001337341,0.0003108791,0.00001159472,0.0091534],"genre_scores_gemma":[0.9992915,0.0001376321,0.00009464232,0.0001161092,0.00002490609,0.000003126783,0.00009304423,0.000003493599,0.0002353964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00506293,"threshold_uncertainty_score":0.02162337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07063289804850297,"score_gpt":0.2634005148274235,"score_spread":0.1927676167789206,"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."}}