{"id":"W4414948547","doi":"10.1155/atr/8545604","title":"The In‐Depth Analysis on the Influencing Factors of Urban Vitality in China’s HSR Station Area","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Aviation Industry Analysis and Trends","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing Social Science Fund; Beijing Union University; National Natural Science Foundation of China","keywords":"Vitality; Ordinary least squares; Regression analysis; Urban area; Urban planning; Geographically Weighted Regression; Association rule learning; Driving factors","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006816881,0.0000779551,0.0003023664,0.0005783789,0.00006315978,0.00001882324,0.0001222147,0.00004766521,0.00002342195],"category_scores_gemma":[0.0001095541,0.00005401002,0.0001847094,0.00146722,0.00002569065,0.0002708628,0.000001298021,0.0002166417,4.002972e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001008594,"about_ca_system_score_gemma":0.00002563382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001866137,"about_ca_topic_score_gemma":0.001688405,"domain_scores_codex":[0.9986059,0.00002900421,0.001094154,0.0001069372,0.00007171044,0.00009227687],"domain_scores_gemma":[0.9985709,0.0001641436,0.001052778,0.000131066,0.0000641587,0.00001690571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004134997,0.00004576505,0.749349,0.000005547804,0.00014104,8.327626e-7,0.001561412,0.2222704,0.00001741515,0.02608497,0.000005998968,0.0004762415],"study_design_scores_gemma":[0.0003242922,0.00003142944,0.9900992,0.00003204654,0.0000534296,3.198576e-8,0.001411365,0.0005565076,0.0001727527,0.007159197,0.0001138683,0.00004588687],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957977,0.0002109116,0.002721964,0.0005867487,0.00008471731,0.00005544727,0.00003105343,0.000001639499,0.0005098491],"genre_scores_gemma":[0.9997329,0.0001087603,0.00005131022,0.00002333963,0.000007341242,0.000002918829,0.00002047944,0.000003064442,0.00004985437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2407502,"threshold_uncertainty_score":0.2202464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01941411500718361,"score_gpt":0.2496557413072888,"score_spread":0.2302416263001052,"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."}}