{"id":"W2137872550","doi":"10.5539/ibr.v3n3p194","title":"Development Mode of Automotive Logistics and Optimizing Countermeasure of China’s Automotive Enterprises","year":2010,"lang":"en","type":"article","venue":"International Business Research","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automotive industry; Countermeasure; China; Business; Manufacturing engineering; Industrial organization; Engineering","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":[0.0005465717,0.0001276679,0.0001954104,0.0009693807,0.0001457033,0.00008970204,0.0005324982,0.0001212305,0.0001633389],"category_scores_gemma":[0.001442522,0.0001186445,0.00002747249,0.0007951507,0.0007005224,0.0005096779,0.0005250597,0.0004460952,0.00001832712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003560813,"about_ca_system_score_gemma":0.0001181981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000781867,"about_ca_topic_score_gemma":0.0002828498,"domain_scores_codex":[0.9985168,0.000009584751,0.0003830655,0.0002352335,0.0006349923,0.0002203364],"domain_scores_gemma":[0.994555,0.00009450485,0.000222875,0.0001989702,0.004919015,0.000009622421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004633197,0.002386343,0.4949306,0.00127179,0.0009585192,0.00007580455,0.002469142,0.001637444,0.1274035,0.3357562,0.006407488,0.02623984],"study_design_scores_gemma":[0.001244657,0.00002249599,0.9431429,0.0004589482,0.00003422308,0.00001610237,0.001331373,0.006641281,0.01878146,0.01161978,0.01631948,0.0003873053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9718434,0.00001556167,0.005697549,0.004465219,0.0004644183,0.0002147117,0.00002473487,0.00006599032,0.01720838],"genre_scores_gemma":[0.9949566,0.00001407308,0.004514696,0.0001005479,0.0001672083,0.00002277685,0.00005871791,0.00001656638,0.0001488609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4482123,"threshold_uncertainty_score":0.4838181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05101390548749513,"score_gpt":0.3438016643557466,"score_spread":0.2927877588682514,"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."}}