{"id":"W4379390809","doi":"10.32920/23296070","title":"Shopping Centre Classifications: A Dynamic Approach","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Consumer Retail Behavior Studies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Metropolitan area; Hierarchy; Analytics; Computer science; Multivariate statistics; Business; Data science; Geography; Machine learning; Economics","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":[0.005065246,0.0005760069,0.0007337994,0.009244155,0.002577584,0.01269292,0.003115835,0.001543843,0.008938977],"category_scores_gemma":[0.01394717,0.0007253028,0.001136193,0.01070029,0.002369262,0.01049054,0.004541382,0.00190458,0.003453916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005415828,"about_ca_system_score_gemma":0.003841104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03108275,"about_ca_topic_score_gemma":0.03509324,"domain_scores_codex":[0.9933386,0.002141111,0.0004929364,0.00188704,0.001614053,0.000526159],"domain_scores_gemma":[0.9902331,0.002478542,0.0009018216,0.002123105,0.003697588,0.0005657862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003899166,0.0004073447,0.1049787,0.0004097596,0.0002098727,0.0002409703,0.01082642,0.01746487,0.00270952,0.2937154,0.03396365,0.5346835],"study_design_scores_gemma":[0.00005726567,0.0001798494,0.09975254,0.0005549811,0.0002175715,0.0006401694,0.02624687,0.3283634,0.00290249,0.251683,0.289088,0.0003139051],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1404865,0.0007881587,0.7291318,0.004961023,0.0003360745,0.001408905,0.01120748,0.00257686,0.1091032],"genre_scores_gemma":[0.6166275,0.0004335341,0.3552477,0.0003800353,0.0001540612,0.0007944559,0.008561048,0.0005275935,0.01727417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03108275,"threshold_uncertainty_score":0.06180364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1033237682343444,"score_gpt":0.2856103059214971,"score_spread":0.1822865376871527,"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."}}