{"id":"W3087781799","doi":"10.1155/2020/8885808","title":"Research on Coordinated Development of a Railway Freight Collection and Distribution System Based on an “Entropy-TOPSIS Coupling Development Degree Model” Integrated with Machine Learning","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transport and Logistics Innovations","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"TOPSIS; Transport engineering; Data collection; Entropy (arrow of time); Construct (python library); Distribution (mathematics); Operations research; Engineering; Computer science; Computer network","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":[0.00158731,0.0008147082,0.000503389,0.002150094,0.0007047024,0.002051051,0.0009631371,0.0005355975,0.002607854],"category_scores_gemma":[0.002779457,0.0003873482,0.001260584,0.002002531,0.001241855,0.004039108,0.001399687,0.0007779251,0.0001985801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00302285,"about_ca_system_score_gemma":0.002466521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01195577,"about_ca_topic_score_gemma":0.006241315,"domain_scores_codex":[0.9983107,0.0004352518,0.0001066635,0.0003747794,0.0006153425,0.0001572252],"domain_scores_gemma":[0.9991102,0.0003017563,0.0001708496,0.00004331028,0.0002990787,0.00007491114],"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":[0.00007337622,0.0001098971,0.01426194,0.0004061534,0.0002448195,0.0003802505,0.001392095,0.6376698,0.005986637,0.2533214,0.001290841,0.08486279],"study_design_scores_gemma":[0.00001615552,0.00009573812,0.004035716,0.00003876901,0.00007826186,0.0001090819,0.0004634012,0.9421351,0.001846019,0.04798259,0.003155134,0.00004415689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08467177,0.0004242067,0.8965017,0.0004330679,0.00003933632,0.0001295254,0.0000863364,0.0002056638,0.01750845],"genre_scores_gemma":[0.9421953,0.0005666562,0.05279882,0.00003472614,0.00002145267,0.0001382295,0.0001172906,0.0000369323,0.004090526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01195577,"threshold_uncertainty_score":0.02377236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04443996233408704,"score_gpt":0.2667907320435694,"score_spread":0.2223507697094824,"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."}}