{"id":"W2104114670","doi":"10.1007/s11146-015-9524-1","title":"Predicting Risks of Anchor Store Openings and Closings","year":2015,"lang":"en","type":"article","venue":"The Journal of Real Estate Finance and Economics","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Closing (real estate); Sample (material); Logit; Competition (biology); Probit; Econometrics; Abandonment (legal); Odds; Space (punctuation); Restructuring; Cluster (spacecraft); Ordered probit; Probit model; Business; Computer science; Economics; Statistics; Logistic regression; Finance; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.001039966,0.00008400271,0.0002059327,0.00007130713,0.00008174391,0.0000745236,0.0001191111,0.00002751423,0.000002676336],"category_scores_gemma":[0.00003901609,0.00006245778,0.00002688386,0.00005878654,0.0000792659,0.0007936643,0.00009922958,0.0001307251,9.051029e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001224983,"about_ca_system_score_gemma":0.00003097369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00184988,"about_ca_topic_score_gemma":0.0001424223,"domain_scores_codex":[0.9994504,0.000009009776,0.000310758,0.00006622046,0.00004777141,0.0001158523],"domain_scores_gemma":[0.9991341,0.00005957398,0.0006142149,0.00007747536,0.00009991028,0.00001477353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004382499,0.00001423563,0.7171723,0.00007007393,0.00002761438,0.000006641614,0.001800503,0.0002801807,0.00009243047,0.0002724074,0.0004785692,0.2793468],"study_design_scores_gemma":[0.003721139,0.0002609036,0.8857098,0.00044416,0.0006733558,0.0002785086,0.006474074,0.0160682,0.0002652638,0.003020198,0.08256143,0.0005229358],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959276,0.000241558,0.00001226536,0.0002319821,0.0001565923,0.00005460627,0.000002169273,0.00000403117,0.003369176],"genre_scores_gemma":[0.9918286,0.007755865,0.0001290028,0.00006348575,0.0001792077,3.782148e-7,6.85201e-7,0.000009428026,0.0000333452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2788239,"threshold_uncertainty_score":0.2796477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06035856206761468,"score_gpt":0.2679573443492543,"score_spread":0.2075987822816397,"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."}}