{"id":"W3126098523","doi":"","title":"How do Land Markets Anticipate Regulatory Change? Evidence from Canadian Conservation Policy","year":2016,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Anticipation (artificial intelligence); Lease; Natural resource economics; Economics; Value (mathematics); Land use, land-use change and forestry; Business; Land use; Public economics; Monetary economics; Finance; Ecology","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":[0.002110912,0.0001832701,0.0002767942,0.0008794583,0.001394164,0.002595196,0.0008045377,0.00103355,0.009308513],"category_scores_gemma":[0.01493124,0.0002067374,0.0005111124,0.001719007,0.001708739,0.001652187,0.0005980739,0.00105707,0.0004791563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02307062,"about_ca_system_score_gemma":0.01598301,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9724653,"about_ca_topic_score_gemma":0.9796348,"domain_scores_codex":[0.9986402,0.0001602681,0.00003740303,0.0001611649,0.0006443276,0.0003566098],"domain_scores_gemma":[0.9902361,0.00343921,0.002451017,0.000519509,0.002784721,0.0005694625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000839199,0.0004102304,0.7494945,0.0005280238,0.000388644,0.0004754298,0.004484489,0.02586552,0.002086693,0.05426949,0.04628161,0.1148762],"study_design_scores_gemma":[0.00006660962,0.00004587421,0.9455191,0.0000851362,0.0001205107,0.00003065777,0.002460979,0.006507924,0.0005452352,0.006538008,0.03802305,0.00005693729],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8187056,0.003342657,0.00169881,0.01936238,0.00004917133,0.00008785597,0.003876662,0.00007843106,0.1527985],"genre_scores_gemma":[0.9926749,0.001492009,0.0002376169,0.0006452744,0.00001853859,0.000009174573,0.0007912116,0.00001111217,0.004120122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02753472,"threshold_uncertainty_score":0.1673898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1855923297126252,"score_gpt":0.3001430289768459,"score_spread":0.1145506992642207,"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."}}