{"id":"W4220971956","doi":"10.18280/jesa.550107","title":"Research on Agricultural Logistics Efficiency Based on DEA and Tobit Regression Models","year":2022,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Northeast Agricultural University; National Natural Science Foundation of China","keywords":"Tobit model; Agriculture; Business; Agricultural economics; Index (typography); Agricultural productivity; Regression analysis; Consumption (sociology); Economics; Geography; Econometrics; Computer science","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":[0.01488653,0.002446761,0.001802814,0.008824683,0.0008438114,0.005443076,0.00182664,0.001065906,0.002640964],"category_scores_gemma":[0.04399585,0.0009258837,0.002932744,0.02116973,0.0009560538,0.005448428,0.001869239,0.002563876,0.00096377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00279914,"about_ca_system_score_gemma":0.002315319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009509902,"about_ca_topic_score_gemma":0.006702358,"domain_scores_codex":[0.9825329,0.01115256,0.001301741,0.00162141,0.002613024,0.0007785095],"domain_scores_gemma":[0.9626284,0.02681301,0.003888743,0.00206003,0.004246611,0.0003631116],"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":[0.0004394368,0.0009055555,0.3520088,0.002087607,0.005304424,0.0004949959,0.002359244,0.3744385,0.001187999,0.08054253,0.006084255,0.1741466],"study_design_scores_gemma":[0.00007742681,0.0005529911,0.1078941,0.0006405696,0.001031378,0.0003299569,0.003901006,0.8282528,0.002174159,0.03840507,0.01654003,0.0002005994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5491378,0.006395333,0.4088974,0.001517891,0.000251721,0.0007067922,0.002848958,0.0006179308,0.02962616],"genre_scores_gemma":[0.9550571,0.00317553,0.03591752,0.0001053831,0.00006698691,0.0004230914,0.002217106,0.00009788176,0.002939392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01488653,"threshold_uncertainty_score":0.07872844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06810553572996711,"score_gpt":0.3058230303052384,"score_spread":0.2377174945752713,"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."}}