{"id":"W3205923895","doi":"10.1155/2021/1733579","title":"Exploring Passengers’ Dependency Variety on Stations’ Functions in Urban Subway","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; Beijing Talents Fund; National Natural Science Foundation of China","keywords":"Dependency (UML); Beijing; Transport engineering; Variety (cybernetics); Computer science; Subway station; Plan (archaeology); Geography; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001854047,0.0004320061,0.0002551842,0.001620353,0.0003279283,0.0006854989,0.0002309652,0.0002310632,0.001419071],"category_scores_gemma":[0.001087333,0.0001491148,0.0004168534,0.001675327,0.00022513,0.0008707772,0.0005800472,0.0003180614,0.0003466121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003702902,"about_ca_system_score_gemma":0.0003146095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02072098,"about_ca_topic_score_gemma":0.03527038,"domain_scores_codex":[0.9998065,0.00003776615,0.00001182503,0.00006041555,0.00003579636,0.00004777746],"domain_scores_gemma":[0.9995771,0.00013154,0.0001005456,0.0000571977,0.00008206243,0.00005153867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003198453,0.00009772817,0.9114292,0.000168342,0.0001546275,0.0004505316,0.004027132,0.01081839,0.007712782,0.001500625,0.002351518,0.06096925],"study_design_scores_gemma":[0.000006279771,0.00007280491,0.9212705,0.00003186826,0.00008225987,0.0002918652,0.006410778,0.06484778,0.001871283,0.00124735,0.003830539,0.00003670695],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989553,0.0001079575,0.006825769,0.00007191244,0.000004985166,0.00001599487,0.001774369,0.00008007859,0.001565938],"genre_scores_gemma":[0.996172,0.00007287064,0.002072933,0.000006139201,0.000003370812,0.00001086455,0.00130019,0.000008811653,0.0003528465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02072098,"threshold_uncertainty_score":0.04120076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05574186859428332,"score_gpt":0.2995147136121566,"score_spread":0.2437728450178733,"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."}}