{"id":"W4366705304","doi":"10.2139/ssrn.4276452","title":"Quantifying the Impacts of Climate Shocks in Commercial Real Estate Market","year":2022,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Climate risk; Real estate; Portfolio; Capitalization; Business; Climate change; Natural resource economics; Economics; Finance; Financial economics","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01066805,0.0003276554,0.0008867828,0.0005070877,0.0003128414,0.0001624213,0.0009704976,0.0002222021,0.0005491304],"category_scores_gemma":[0.0001225798,0.0003340057,0.0003986474,0.0002194474,0.00008204872,0.0001578262,0.0007383907,0.005461668,0.0000200992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002527238,"about_ca_system_score_gemma":0.001275967,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004106361,"about_ca_topic_score_gemma":0.01981273,"domain_scores_codex":[0.9953333,0.0001628363,0.001535648,0.0004452241,0.00007569211,0.002447372],"domain_scores_gemma":[0.9975433,0.0001627264,0.001654885,0.0005346414,0.00002671273,0.0000777497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007002069,0.0002509133,0.789468,0.0001874645,0.0004294853,0.0000194966,0.002690844,0.004024326,0.000003532862,0.1239247,0.0003699783,0.07793105],"study_design_scores_gemma":[0.003815468,0.00065584,0.264824,0.0002659943,0.0001223427,0.0003167466,0.007606009,0.01713826,0.000008086269,0.677812,0.02530043,0.00213479],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8673381,0.001463722,0.0001393742,0.0007133508,0.001361679,0.0002693914,0.0001414228,0.00001900615,0.128554],"genre_scores_gemma":[0.7330536,0.266463,0.00004383758,0.00004110499,0.0001969893,0.00001335368,0.00002239952,0.00005560288,0.0001101501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5538874,"threshold_uncertainty_score":0.9999112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03315754009423887,"score_gpt":0.2659078145620484,"score_spread":0.2327502744678095,"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."}}