{"id":"W7132128568","doi":"","title":"盒马鲜生的六大商业逻辑","year":2019,"lang":"","type":"article","venue":"CEIBS Institutional Repository","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Casa","funders":"","keywords":"","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001505456,0.0003623642,0.0003396067,0.0001848952,0.0004562083,0.00005428946,0.0004339792,0.0005880958,0.0006659756],"category_scores_gemma":[0.00003167374,0.0004001414,0.000198085,0.0002487595,0.0005677328,0.0004936854,0.00009715441,0.0007651082,0.002342345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003170623,"about_ca_system_score_gemma":0.0004450009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003744788,"about_ca_topic_score_gemma":0.000004272639,"domain_scores_codex":[0.9981196,0.0000402675,0.0005183977,0.0004994369,0.0003495629,0.0004727489],"domain_scores_gemma":[0.9989731,0.00007417799,0.00005928068,0.0006751462,0.00009025513,0.0001280664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004988528,0.0001004758,0.005646233,0.0003815067,0.0002674986,0.0003465824,0.0002116213,0.04487836,0.01347524,0.9317362,0.001363832,0.001542596],"study_design_scores_gemma":[0.005435453,0.001234688,0.1304057,0.002306383,0.000519067,0.006130471,0.002600902,0.08199012,0.04546217,0.1122427,0.6059472,0.005725176],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3717335,0.008696077,0.0004568024,0.00006910538,0.006624011,0.0002221434,0.00001329746,0.0003523036,0.6118328],"genre_scores_gemma":[0.9876376,0.0002951856,0.000477558,0.00005890653,0.0004248348,0.00002082499,0.00001911475,0.00002616547,0.01103985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8194935,"threshold_uncertainty_score":0.999845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005954313587101258,"score_gpt":0.1901042230396821,"score_spread":0.1841499094525808,"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."}}