{"id":"W4396915944","doi":"10.2139/ssrn.4829218","title":"Anticipation of Retail Gentrification Through Data Science Approach in Seoul, South Korea","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Korean Urban and Social Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gentrification; Anticipation (artificial intelligence); Economic geography; Geography; Regional science; Citizen science; Advertising; Business; Economic growth; Economics; Computer science","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003868163,0.0002059621,0.0003040069,0.0001123846,0.0002199949,0.00009153091,0.001378702,0.0001447977,0.00003880026],"category_scores_gemma":[0.0001235897,0.0001808411,0.00007925859,0.0006414785,0.0006435009,0.0003103417,0.002037148,0.002518722,0.00004766946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00230319,"about_ca_system_score_gemma":0.00133152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001037081,"about_ca_topic_score_gemma":0.0006474099,"domain_scores_codex":[0.9966224,0.00009628234,0.000495383,0.0006709883,0.0007808763,0.001334113],"domain_scores_gemma":[0.9989508,0.00001750858,0.0003590571,0.0005938596,0.0000299681,0.00004875833],"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":[0.0003198022,0.001809659,0.3609969,0.0007463255,0.00111552,0.00002889793,0.1120867,0.01413486,0.004963474,0.3582545,0.002289242,0.1432542],"study_design_scores_gemma":[0.0008574837,0.0002061563,0.07457951,0.0002787841,0.0003825095,0.00007524935,0.0195553,0.01834282,0.0003994031,0.8832213,0.001045744,0.001055787],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959661,0.006804006,0.00665946,0.0005348318,0.0005057606,0.0005662819,0.00005723802,0.00004266375,0.02516875],"genre_scores_gemma":[0.9933071,0.005503641,0.0006008002,0.00001635027,0.0001403285,0.000007898559,0.00004656842,0.00001954362,0.0003577648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5249668,"threshold_uncertainty_score":0.9997825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05765258167630765,"score_gpt":0.2937784368247925,"score_spread":0.2361258551484849,"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."}}