{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001342516,0.0001953684,0.0002190779,0.0009801176,0.002423209,0.0004474409,0.0004636339,0.00007392051,0.0001854712],"category_scores_gemma":[0.0002728976,0.0001393377,0.00006535776,0.001514351,0.0002284231,0.0004340395,0.0003516695,0.001124105,0.00008677816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000184576,"about_ca_system_score_gemma":0.00004314658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005922046,"about_ca_topic_score_gemma":0.000008693825,"domain_scores_codex":[0.9979286,0.0001336624,0.0004061125,0.0002744137,0.0008311166,0.0004261142],"domain_scores_gemma":[0.9988283,0.0002110879,0.0003042487,0.0002714409,0.0003601816,0.00002475739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001749909,0.001136725,0.007003398,0.0003354479,0.00007037807,0.0006134763,0.0002173596,0.116972,0.001166972,0.6265845,0.119334,0.1263907],"study_design_scores_gemma":[0.002000565,0.0008427509,0.265106,0.0008884117,0.00008707132,0.0004098264,0.003766666,0.6348627,0.00009261064,0.07604395,0.0151071,0.0007923801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9366812,0.000204816,0.002514387,0.006701502,0.0005219435,0.0003702185,0.00001190525,0.0003991367,0.05259492],"genre_scores_gemma":[0.9974287,0.00001469924,0.0004621464,0.001189412,0.0003016563,0.00002651219,0.00001591491,0.00002904646,0.0005318699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5505406,"threshold_uncertainty_score":0.9988755,"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."}}