{"id":"W3086229732","doi":"10.1155/2020/2109423","title":"Construction of Regional Logistics Weighted Network Model and Its Robust optimization: Evidence from China","year":2020,"lang":"en","type":"article","venue":"Complexity","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Ministry of Education of the People's Republic of China; National Natural Science Foundation of China; Australian Research Council; Education Department of Hunan Province","keywords":"Construct (python library); Computer science; China; Index (typography); Operations research; Scale (ratio); Business; Complex network; Gravity model of trade; Weighted network; Industrial organization; Mathematics; Computer network; International trade; Geography","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":[0.00125006,0.0006206678,0.0006306884,0.0007923231,0.0006178693,0.001226796,0.001018541,0.0006014652,0.00268628],"category_scores_gemma":[0.003755681,0.0002860853,0.0009429394,0.0008622138,0.0008308625,0.002198359,0.0009486308,0.0008427492,0.0002040478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002022112,"about_ca_system_score_gemma":0.001435453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04212391,"about_ca_topic_score_gemma":0.02126347,"domain_scores_codex":[0.9995576,0.0002055137,0.00001537383,0.0001008531,0.00004892992,0.00007172683],"domain_scores_gemma":[0.9985939,0.0006620885,0.0003136898,0.00009984718,0.0002276484,0.0001029058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007573989,0.00005778705,0.01366155,0.0001095901,0.0001233483,0.0002654779,0.0001780678,0.9021104,0.0003308709,0.06944352,0.002213723,0.01143001],"study_design_scores_gemma":[0.00001345126,0.00001999705,0.002494978,0.00001216629,0.00003724433,0.00002374806,0.0001581656,0.9665322,0.0001155525,0.02951492,0.001064597,0.00001310152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8300149,0.00124001,0.152047,0.001564987,0.00004044687,0.00009091099,0.000499712,0.0001663637,0.01433565],"genre_scores_gemma":[0.9901251,0.0005877605,0.007287645,0.00004976354,0.00001317507,0.00004292193,0.0003152001,0.00003234591,0.001546156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04212391,"threshold_uncertainty_score":0.08375746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1273605475546547,"score_gpt":0.2804059680266056,"score_spread":0.1530454204719509,"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."}}