{"id":"W3212917608","doi":"10.32866/001c.29523","title":"Discovering Millennials’ Migration Clusters in Seoul, South Korea: A Local Spatial Network Autocorrelation Approach","year":2021,"lang":"en","type":"article","venue":"Findings","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Metropolitan area; Economic geography; Spatial analysis; Geography; Autocorrelation; Cartography; Regional science; Statistics; Mathematics; Remote sensing; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005754366,0.0001713134,0.0001809227,0.002136883,0.0004353417,0.0004624413,0.0003557756,0.0001366142,0.0006969413],"category_scores_gemma":[0.001820544,0.0001108913,0.0002955071,0.001945487,0.0001675637,0.0004535691,0.0007740695,0.0002079043,0.0001209851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005510886,"about_ca_system_score_gemma":0.0006805657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03209135,"about_ca_topic_score_gemma":0.06442931,"domain_scores_codex":[0.9997796,0.00007196981,0.00001856655,0.00006595301,0.00002240476,0.00004140705],"domain_scores_gemma":[0.9991366,0.0002458224,0.0002434274,0.00008006435,0.0001855115,0.0001085014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008688826,0.00005392623,0.9638986,0.0000480981,0.00009966493,0.0002109876,0.0009911889,0.006444401,0.0005762838,0.001073774,0.0009045342,0.02561161],"study_design_scores_gemma":[0.00001153925,0.00006998894,0.8633364,0.00006844092,0.0001438771,0.0002022532,0.01018899,0.1211014,0.0007120385,0.001674896,0.002457427,0.00003269993],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969008,0.00007639501,0.002172112,0.00004297482,0.000003399165,0.00001434456,0.0003849228,0.00001421638,0.0003908],"genre_scores_gemma":[0.9971291,0.00004536411,0.002134915,0.000005066625,0.000001781062,0.00001109548,0.0005030631,0.000003055617,0.0001665724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03209135,"threshold_uncertainty_score":0.0638091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02456870673365079,"score_gpt":0.1983533319854543,"score_spread":0.1737846252518035,"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."}}